Image Machine

imagemachine

Developers are always concerned about their code quality. And they try their best to ensure the best quality of their code. If they find some tools that can show them exactly where they need to make changes to improve the quality of the code. There are many of them, but I will explain how you can set up SonarQube locally with docker so that you can evaluate your project code quality. Without wasting any time let’s begin. Prerequisite You must have docker installed in your system. If you don’t have it installed, then please feel free to set up Docker using this link. Docker Container On your root project, create a docker-compose.yml file, and then we will need a database so that Sonarqube can store its analytical data. postgres:
    image: postgres
    restart: always
    environment:
      POSTGRES_PASSWORD: example
    networks:
      - lookup
    ports:
      - 5432:5432
    volumes:
      - postgres:/var/lib/postgresql/data After that, we will need Sonarqube service, sonarqube:
    image: sonarqube:latest
    restart: always
    ports:
      - 9000:9000
    environment:
      SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres
      SONARQUBE_JDBC_USERNAME: postgres
      SONARQUBE_JDBC_PASSWORD: example
    networks:
      - lookup
    depends_on:
      - postgres Don’t worry; I will explain each line. sonarqube:
This defines a service in your Docker Compose file. The name of the service is sonarqube, meaning that this section is describing how the SonarQube container will run.


image: sonarqube:latest
This specifies the Docker image to use for the SonarQube service. In this case, it pulls the latest version of the official SonarQube image from Docker Hub.


restart: always
This ensures that the SonarQube container will automatically restart if it crashes or if the Docker host is rebooted.


ports:
- 9000:9000:
This maps port 9000 on the host machine to port 9000 inside the container. SonarQube’s web interface runs on port 9000, so this makes it accessible from the host machine's browser at http://localhost:9000.


SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres
Defines the JDBC URL to connect SonarQube to a PostgreSQL database.


SONARQUBE_JDBC_USERNAME: postgres:
The username used to authenticate with the PostgreSQL database. In this case, it's postgres.


SONARQUBE_JDBC_PASSWORD: example:
The password used to authenticate the PostgreSQL database, set to example. The final version of our docker-compose.yml file will be the following services:
  postgres:
    image: postgres
    restart: always
    environment:
      POSTGRES_PASSWORD: example
    networks:
      - lookup
    ports:
      - 5432:5432
    volumes:
      - postgres:/var/lib/postgresql/data

  sonarqube:
    image: sonarqube:latest
    restart: always
    ports:
      - 9000:9000
    environment:
      SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres
      SONARQUBE_JDBC_USERNAME: postgres
      SONARQUBE_JDBC_PASSWORD: example
    networks:
      - lookup
    depends_on:
      - postgres

networks:
  lookup:

volumes:
  postgres:
    driver: local Inside your project directory, open a terminal, and to start and run the services do docker-compose up . Setup SonarQube Once the services are running on a browser, go to localhost:9000, and it will open the Sonarqube log-in page. The default username and password is admin. Once you are logged in, it will ask you to set up a new password. After completing these steps, you will be able to see the Sonarqube dashboard. As we are running the Sonarqube locally, we will create a local project. In the below, you will see the marked section that you have to click. It will ask for a project name and a branch name. I will leave the branch name as main. You can choose any branch that you have and create a project. Then we will see a new section where we have to choose Use the global setting  for now. Once we click on  create project, it should ask to generate a token. I will only update the Expires in section and make it to No Expiration. Once we generate a token, it will ask us to select our project coding language; after choosing that, we need to download the scanner zip folder. I have marked the section where you will find it below. Let’s unzip the folder and open the folder; you will find the bin folder, and inside, you will find a sonar-scanner file. Copy the path of that file, and save it in a text file. ~./sonar-scanner-cli-6.1.0.4477-linux-x64/sonar-scanner-6.1.0.4477-linux-x64/bin After that, you need to copy the command given in the Execute the Scanner section. Once you copy that, then concat with the path that you have saved in the file as shown below. ~./sonar-scanner-cli-6.1.0.4477-linux-x64/sonar-scanner-6.1.0.4477-linux-x64/bin/sonar-scanner \
   -Dsonar.projectKey=demo   \
   -Dsonar.sources=.   \
   -Dsonar.host.url=http://localhost:9000   \
   -Dsonar.token=<Your_Token> Once you execute the command in the terminal, it will start analyzing your code. Once it finishes the operation, you should see some analytical information in the Sonarqube dashboard. And then, you should be able to see some metrics, and if you have any issues, you should be able to see the reasons for marking it as an issue. Great, we have completed the setup of our Sonarqube on our project locally. Developers are always concerned about their code quality. And they try their best to ensure the best quality of their code. If they find some tools that can show them exactly where they need to make changes to improve the quality of the code. There are many of them, but I will explain how you can set up SonarQube locally with docker so that you can evaluate your project code quality. Without wasting any time let’s begin. Prerequisite You must have docker installed in your system. If you don’t have it installed, then please feel free to set up Docker using this link . link Docker Container On your root project, create a docker-compose.yml file, and then we will need a database so that Sonarqube can store its analytical data. postgres:
    image: postgres
    restart: always
    environment:
      POSTGRES_PASSWORD: example
    networks:
      - lookup
    ports:
      - 5432:5432
    volumes:
      - postgres:/var/lib/postgresql/data postgres:
    image: postgres
    restart: always
    environment:
      POSTGRES_PASSWORD: example
    networks:
      - lookup
    ports:
      - 5432:5432
    volumes:
      - postgres:/var/lib/postgresql/data After that, we will need Sonarqube service, sonarqube:
    image: sonarqube:latest
    restart: always
    ports:
      - 9000:9000
    environment:
      SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres
      SONARQUBE_JDBC_USERNAME: postgres
      SONARQUBE_JDBC_PASSWORD: example
    networks:
      - lookup
    depends_on:
      - postgres sonarqube:
    image: sonarqube:latest
    restart: always
    ports:
      - 9000:9000
    environment:
      SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres
      SONARQUBE_JDBC_USERNAME: postgres
      SONARQUBE_JDBC_PASSWORD: example
    networks:
      - lookup
    depends_on:
      - postgres Don’t worry; I will explain each line. sonarqube:
This defines a service in your Docker Compose file. The name of the service is sonarqube, meaning that this section is describing how the SonarQube container will run. image: sonarqube:latest
This specifies the Docker image to use for the SonarQube service. In this case, it pulls the latest version of the official SonarQube image from Docker Hub. restart: always
This ensures that the SonarQube container will automatically restart if it crashes or if the Docker host is rebooted. ports:
- 9000:9000:
This maps port 9000 on the host machine to port 9000 inside the container. SonarQube’s web interface runs on port 9000, so this makes it accessible from the host machine's browser at http://localhost:9000. SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres
Defines the JDBC URL to connect SonarQube to a PostgreSQL database. SONARQUBE_JDBC_USERNAME: postgres:
The username used to authenticate with the PostgreSQL database. In this case, it's postgres. SONARQUBE_JDBC_PASSWORD: example:
The password used to authenticate the PostgreSQL database, set to example. sonarqube: This defines a service in your Docker Compose file. The name of the service is sonarqube, meaning that this section is describing how the SonarQube container will run. sonarqube: sonarqube: This defines a service in your Docker Compose file. The name of the service is sonarqube , meaning that this section is describing how the SonarQube container will run. sonarqube image: sonarqube:latest This specifies the Docker image to use for the SonarQube service. In this case, it pulls the latest version of the official SonarQube image from Docker Hub. image: sonarqube:latest image: sonarqube:latest This specifies the Docker image to use for the SonarQube service. In this case, it pulls the latest version of the official SonarQube image from Docker Hub. restart: always This ensures that the SonarQube container will automatically restart if it crashes or if the Docker host is rebooted. restart: always restart: always This ensures that the SonarQube container will automatically restart if it crashes or if the Docker host is rebooted. ports: - 9000:9000: This maps port 9000 on the host machine to port 9000 inside the container. SonarQube’s web interface runs on port 9000, so this makes it accessible from the host machine's browser at http://localhost:9000. ports: ports: - 9000:9000 : - 9000:9000 This maps port 9000 on the host machine to port 9000 inside the container. SonarQube’s web interface runs on port 9000, so this makes it accessible from the host machine's browser at http://localhost:9000 . http://localhost:9000 SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres Defines the JDBC URL to connect SonarQube to a PostgreSQL database. SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres Defines the JDBC URL to connect SonarQube to a PostgreSQL database. SONARQUBE_JDBC_USERNAME: postgres: The username used to authenticate with the PostgreSQL database. In this case, it's postgres. SONARQUBE_JDBC_USERNAME: postgres : SONARQUBE_JDBC_USERNAME: postgres The username used to authenticate with the PostgreSQL database. In this case, it's postgres . postgres SONARQUBE_JDBC_PASSWORD: example: The password used to authenticate the PostgreSQL database, set to example. SONARQUBE_JDBC_PASSWORD: example : SONARQUBE_JDBC_PASSWORD: example The password used to authenticate the PostgreSQL database, set to example . example The final version of our docker-compose.yml file will be the following services:
  postgres:
    image: postgres
    restart: always
    environment:
      POSTGRES_PASSWORD: example
    networks:
      - lookup
    ports:
      - 5432:5432
    volumes:
      - postgres:/var/lib/postgresql/data

  sonarqube:
    image: sonarqube:latest
    restart: always
    ports:
      - 9000:9000
    environment:
      SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres
      SONARQUBE_JDBC_USERNAME: postgres
      SONARQUBE_JDBC_PASSWORD: example
    networks:
      - lookup
    depends_on:
      - postgres

networks:
  lookup:

volumes:
  postgres:
    driver: local services:
  postgres:
    image: postgres
    restart: always
    environment:
      POSTGRES_PASSWORD: example
    networks:
      - lookup
    ports:
      - 5432:5432
    volumes:
      - postgres:/var/lib/postgresql/data

  sonarqube:
    image: sonarqube:latest
    restart: always
    ports:
      - 9000:9000
    environment:
      SONARQUBE_JDBC_URL: jdbc:postgresql://postgres:5432/postgres
      SONARQUBE_JDBC_USERNAME: postgres
      SONARQUBE_JDBC_PASSWORD: example
    networks:
      - lookup
    depends_on:
      - postgres

networks:
  lookup:

volumes:
  postgres:
    driver: local Inside your project directory, open a terminal, and to start and run the services do docker-compose up . docker-compose up Setup SonarQube Once the services are running on a browser, go to localhost:9000 , and it will open the Sonarqube log-in page. The default username and password is admin . Once you are logged in, it will ask you to set up a new password. localhost:9000 admin After completing these steps, you will be able to see the Sonarqube dashboard. As we are running the Sonarqube locally, we will create a local project. In the below, you will see the marked section that you have to click. It will ask for a project name and a branch name. I will leave the branch name as main . You can choose any branch that you have and create a project. Then we will see a new section where we have to choose Use the global setting for now. main Use the global setting Once we click on create project , it should ask to generate a token. I will only update the Expires in section and make it to No Expiration . Once we generate a token, it will ask us to select our project coding language; after choosing that, we need to download the scanner zip folder. I have marked the section where you will find it below. create project Expires in No Expiration Let’s unzip the folder and open the folder; you will find the bin folder, and inside, you will find a sonar-scanner file. Copy the path of that file, and save it in a text file. bin sonar-scanner ~./sonar-scanner-cli-6.1.0.4477-linux-x64/sonar-scanner-6.1.0.4477-linux-x64/bin ~./sonar-scanner-cli-6.1.0.4477-linux-x64/sonar-scanner-6.1.0.4477-linux-x64/bin After that, you need to copy the command given in the Execute the Scanner section. Execute the Scanner Once you copy that, then concat with the path that you have saved in the file as shown below. ~./sonar-scanner-cli-6.1.0.4477-linux-x64/sonar-scanner-6.1.0.4477-linux-x64/bin/sonar-scanner \
   -Dsonar.projectKey=demo   \
   -Dsonar.sources=.   \
   -Dsonar.host.url=http://localhost:9000   \
   -Dsonar.token=<Your_Token> ~./sonar-scanner-cli-6.1.0.4477-linux-x64/sonar-scanner-6.1.0.4477-linux-x64/bin/sonar-scanner \
   -Dsonar.projectKey=demo   \
   -Dsonar.sources=.   \
   -Dsonar.host.url=http://localhost:9000   \
   -Dsonar.token=<Your_Token> Once you execute the command in the terminal, it will start analyzing your code. Once it finishes the operation, you should see some analytical information in the Sonarqube dashboard. And then, you should be able to see some metrics, and if you have any issues, you should be able to see the reasons for marking it as an issue. Great, we have completed the setup of our Sonarqube on our project locally.

How to Setup SonarQube Locally Using Docker

Let's learn about via these 64 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Machine Learning Tutorials /Learn Repo 1. Top 9 Free Beginner Tutorials for Machine Learning (ML) This post includes a round-up of some of the best free beginner tutorials for Machine Learning. 2. Golang in Machine Learning Can Golang be used in Machine Learning? In the article you will learn advantages and disadvantages of using Go lang in Machine learning 3. How to Build an Image Search Engine to Find Similar Images After reading this article, you will be able to create a search engine for similar images for your objective from scratch 4. Beat The Heat with Machine Learning Cheat Sheet If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here? 5. 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First, a very large, classified input dataset is needed so that the Neural Network can learn the different features it needs for the classification. 63. Intro to Audio Analysis: Recognizing Sounds Using Machine Learning 64. Loan Risk Prediction Using Neural Networks A Step-by-Step Guide (With a Healthy Dose of Data Cleaning) Thank you for checking out the 64 most read stories about Machine Learning Tutorials on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

64 Stories To Learn About Machine Learning Tutorials

Let's learn about via these 59 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Tensorflow /Learn Repo 1. Quantum Machine Learning Using TensorFlow Quantum INTRODUCTION 2. What Does Facebook Hydra Mean For The Future of Python Ever since its inception in the year 1089 by Guido Van Rossum, the programming language Python has along far away. Sheldon did its creator knew that Python would in today's world be utilized for a variety of purposes such as research, development, scripting, among many others. Built as a successor in the ABC language, Python does not just find its applications in software development but also in research. 3. YouTube's Recommendation Engine: Explained Every successful tech product, by the very definition, is a result of some technological marvels working with impeccable user experience to solve a key problem for the users. One such marvel is the recommendation engine by YouTube. 4. NSFW Filter Introduction: Building a Safer Internet Using AI Filtering out NSFW images with a web extension built using TensorFlow JS. 5. Why ML in Production is (still) Broken and Ways we Can Fix it Machine Learning, Deep Learning development in production was still broken. ZenML, an extensible, open-source MLOps framework for production-ready ML pipelines. 6. Deploy First TensorFlow Model in Android App Simple linear regression is useful for finding the relationship between two continuous variables. One is a predictor or independent variable and the other is a response or dependent variable. It looks for a statistical relationship but not a deterministic relationship. Relationship between two variables is said to be deterministic if one variable can be accurately expressed by the other. For example, using temperature in degrees Celsius it is possible to accurately predict Fahrenheit. 7. How to Run Machine-Learning Models in the Browser using ONNX Learn how to use ONNX Runtime Web to deploy machine-learning models natively to the browser. 8. Training Your Models on Cloud TPUs in 4 Easy Steps on Google Colab You have a plain old TensorFlow model that’s too computationally expensive to train on your standard-issue work laptop. I get it. I’ve been there too, and if I’m being honest, seeing my laptop crash twice in a row after trying to train a model on it is painful to watch. 9. 10 Must-Try Open Source Tools for Machine Learning Machine learning is the future. But will machines ever extinct humans? 10. Efficient Implementation of MobileNet and YOLO Object Detection Algorithms for Image Annotation The objective of the problem is to implement classification and localization algorithms to achieve high object classification and labelling accuracies, and train models readily with as least data and time as possible. The solution to the problem is considered in the following blog. 11. Cocktail Alchemy: Creating New Recipes With Transformers Build a transformer model with natural language processing to create new cocktail recipes from a cocktail database. 12. Auto-Generating Lyrics With TensorFlow and Machine Learning: A How-To Guide Creating a bot that, given a starting phrase, would generate its own lyrics, powered by a machine learning model that would have learned from existing songs. 13. How I Transfer an Artistic Style to Any Image Machine learning and artificial intelligence have been on my radar for years now, but more as a concept and “thing I should know about.” I didn’t feel that I had the free time or skills to dig into it. However, my attitude about machine learning has changed in the past few months. I have seen new and easier tools become accessible to the public. In this post I will walk you through how to transfer an art style to any image using some of these tools. 14. Style Transferring with TensorFlow Style transfer is a computer vision-based technique combined with image processing. Learn about style transfer with Tensorflow, a prominent framework in AI & ML 15. Time Series Forecasting with TensorFlow.js Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework 16. Manipulación de tensores en PyTorch. ¡El primer paso para el deep learning! *Nota: Contactar a Omar Espejel (omar@tsc.ai) para cualquier observación. Cualquier error es responsabilidad del autor. 17. TLDR Newsletter Week of August 5th Highlights 18. Flax: Google’s Open Source Approach To Flexibility In Machine Learning Thinking of Machine Learning, the first frameworks that come to mind are Tensorflow and PyTorch, which are currently the state-of-the-art frameworks if you want to work with Deep Neural Networks. Technology is changing rapidly and more flexibility is needed, so Google researchers are developing a new high performance framework for the open source community: Flax. 19. Top 3 Face Datasets and How to Work with Them An image dataset contains specially selected digital images intended to help train, test, and evaluate an artificial intelligence (AI) or machine learning (ML) 20. PyTorch vs TensorFlow: Who has More Pre-trained Deep Learning Models? Given the importance of pre-trained Deep Learning models, which Deep Learning framework - PyTorch or TensorFlow - has more of these models available to users is 21. Rare Datasets for Computer Vision Every Machine Learning Expert Must Work With Have you ever being in a situation to guess another person’s age? Well May be YES!! How about playing games like finding things in minimum time? or about finding the written character where your doctor wrote in the prescription when you are sick? 22. How I Built an AI to Detect License Plate Number Registration (ANPR) This Car Mod Is A Privacy Nightmare! (AI Number Plate Reader with Python, Tensorflow, OpenCV, OpenALPR) 23. How to Fine Tune a 🤗 (Hugging Face) Transformer Model How to fine-tune a Hugging Face Transformer model for Sequence Classification 24. Top 10 Data Science Libraries in Python Data Science Libraries that will shine this year. 25. What Neural Networks Teach Us About Schizophrenia Pretrained Artificial Neural Networks used to work like a Blackbox: You hand them an input and they predict an output with a certain probability — but without us knowing the internal processes of how they came up with their prediction. A Neural Network to recognize images usually consists of around 20 neuron layers, trained with millions of images to tweak the network parameters to give high quality classifications. 26. Classify Handwritten Digits using Deep learning with Tensorflow Deep learning is a subpart of machine learning and artificial intelligence which is also known as deep neural network this networks capable of learning unsupervised from provided data which is unorganized or unlabeled. today, we will implement a neural network in 6 easy steps using TensorFlow  to classify handwritten digits. 27. Artificial Intelligence Vs Machine Learning: What's the difference? AI and Machine Learning are predominant terms that are creating a lot of buzz in the technology world. The terms can often be used interchangeably but that’s not the case, AI and ML are way more different from each other in their approach, algorithms and logical thinking. 28. How To Build Links Detector That Making Links in Your Book Clickable How I built a link detector for your smart phone to browse links printed in books. 29. Computer Vision Could Improve Health and Workplace Safety Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers. 30. From TF to TFLite: Deploying ML Models on Mobile [Part 2] This is part 2 of the two-part article on deploying ML models on mobile. We saw how to convert our ML models to TfLite format here. For those of you who came here first, I recommend you click on the above link to get the whole picture.  If you just want the android part ,the demo app we are building has a GAN model generating handwritten digits and a classifier model predicting the generated digit. 31. From TF to TFLite: Deploying ML Models on Mobile [Part 1] tl;dr - Link to code: TensorFlow GAN model. So the other day I was talking to my rubber ducky about how G-Board predicts my next word, even when those words are entirely made up by me, in that how it actually learns on-device. How amazingly Netflix, Amazon, Google Maps make use of machine learning in their apps. How does machine learning on apps even work? Does the model learn even after being deployed? Can I deploy a GAN model on mobile? 32. How to Use TensorFlow in Python: Google‘s Open-Source Library For Deep Learning You might not always know it, but Deep Learning is everywhere. We explain how to use TensorFlow, Google's Library For Deep Learning, in Python. 33. Build Your Own Voice Recognition Model with Tensorflow While I'm usually a JavaScript person, there are plenty of things that Python makes easier to do. Doing voice recognition with machine learning is one of those. 34. C++ to WebAssembly using Bazel and Emscripten How to get Bazel and Emscripten to compile C++ to WebAssembly or JavaScript 35. Approach Pre-Trained Deep Learning Models With Caution Pre-trained models are easy to use, but are you glossing over details that could impact your model performance? 36. Tensorflow Vs. Keras: Comparison by building a model for image classification Yes, as the title says, it has been very usual talk among data-scientists (even you!) where a few say, TensorFlow is better and some say Keras is way good! Let’s see how this thing actually works out in practice in the case of image classification. 37. Human Detection System Using RaspberryPi, Thermal Camera and Machine Learning Triggering reliable events based on the presence of people has been the dream of many geeks and DIY automators for a while. Having your house to turn the lights on or off when you enter or exit your living room is an interesting application, for instance. Most of the solutions out there to solve these kinds of problems, even more high-end solutions like the Philips Hue sensors, detect motion, not actual people presence — which means that the lights will switch off once you lay on your couch like a sloth. 38. How Blockchain & AI Integration is Changing Business Landscape? The potential of Blockchain is no lesser than Artificial intelligence. If you have taken a look at them, you must already know the impacts of these technologies on various industries. 39. Lessons for Improving Training Performance — Part 1 Part 1: Lower precision & larger batch size are standard now 40. [Hacking Tinder] Train an AI to Auto-Swipe for You 🖖 Auto-tinder was created to train an AI using Tensorflow and Python3 that learns your interests in the other sex and automatically plays the tinder swiping-game for you. 41. Fabio Manganiello on Home-Made Computer Vision, IoT, Automation, AI Fabio Manganiello writes about solutions he's discovered while building a platform, library of plugins and an API to connect/manage any device and service through any backend, allowing users to easily set up any kind of automation. Fabio is based in Amsterdam, the Netherlands, and has been nominated for a 2020 #Noonie for exceptional contributions to the IoT tag category on Hacker Noon. 42. Efficient Model Training in the Cloud with Kubernetes, TensorFlow, and Alluxio Open Source This article presents the collaboration of Alibaba, Alluxio, and Nanjing University in tackling the problem of Deep Learning model training in the cloud. Various performance bottlenecks are analyzed with detailed optimizations of each component in the architecture. This content was previously published on Alluxio's Engineering Blog, featuring Alibaba Cloud Container Service Team's case study (White Paper here). Our goal was to reduce the cost and complexity of data access for Deep Learning training in a hybrid environment, which resulted in over 40% reduction in training time and cost. 43. The Full Story behind Convolutional Neural Networks and the Math Behind it Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance. [44. Differential Privacy with Tensorflow 2.0 :  Multi class Text Classification Privacy](https://hackernoon.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh) Introduction 45. What Is Edge AI? Edge AI—also referred to as on-device AI—commonly refers to the components required to run an AI algorithm locally on a hardware device. 46. Mueller Report for Nerds! Spark meets NLP with TensorFlow and BERT (Part 1) Photo by Michael on Unsplash 47. [Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV Training a Neural Network from scratch suffers two main problems. First, a very large, classified input dataset is needed so that the Neural Network can learn the different features it needs for the classification. 48. Objects Classification Using CNN-based Model — All the images (plots) are generated and modified by the Author. 49. Can I Grade Loans Better Than LendingClub? In case you missed it, I built a neural network to predict loan risk using a public dataset from LendingClub. Then I built a public API to serve the model’s predictions. That’s nice and all, but… how good is my model? 50. C++ to WebAssembly using Bazel and Emscripten How to get Bazel and Emscripten to compile C++ to WebAssembly or JavaScript 51. Training Your Own Text Classification Model From Scratch With Tensorflow Is As Easy As ABC Hello ML Newb! In this article, you will learn to train your own text classification model from scratch using Tensorflow in just a few lines of code. 52. RethNet Model: Object-by-Object Learning for Detecting Facial Skin Problems In August 2019, a group of researchers from lululab Inc propose the state-of-the-art concept using a semantic segmentation method to detect the most common facial skin problems accurately. The work is accepted to ICCV 2019 Workshop. 53. Uber AI Labs Senior Research Scientist Talks TensorFlow 2.0 [Interview] There’s no doubt that TensorFlow is one of the most popular machine learning libraries right now. However, newbie developers who want to experiment with TensorFlow often face difficulties in learning TensorFlow; the framework has a not unjustified reputation for having a steep learning curve that can make it hard for developers to get to grips with quickly. 54. Training Machine Learning Models Using TensorFlow or PyTorch I will show you how gradient descent works, which is in the deepest deep of machine learning. 55. Building Machine Learning Models With TensorFlow In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models. 56. Build A Smart Baby Monitor Using a RaspberryPi and Tensorflow Some of you may have noticed that it’s been a while since my last article, despite winning this year's IoT Noonies award (btw thanks to all of you who voted, that means a lot to me!). 57. #Switch2Swift for Deep Learning If you are interested, what the recent fast.ai advanced and closed Deep Learning Class had to say about Google’s Swift for Tensorflow project, you might find this post interesting. Even if you attended the class, you should find here hopefully a good overview (with links into the class, presentations, and additional material), what Swift for Tensorflow is and why it might be relevant. 58. Scale Vision Transformers (ViT) Beyond Hugging Face Speed up state-of-the-art ViT models in Hugging Face 🤗 up to 2300% (25x times faster ) with Databricks, Nvidia, and Spark NLP 🚀 59. Loan Risk Prediction Using Neural Networks A Step-by-Step Guide (With a Healthy Dose of Data Cleaning) Thank you for checking out the 59 most read stories about Tensorflow on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

59 Stories To Learn About Tensorflow

Chances are if you have been up to speed with the latest computing technologies, you have probably heard about the concept of quantum computing. Although the terminology can make the field sound a bit intimidating and abstruse, the principles of and its applications can be understood on an intuitive level with enough exposure and experience. quantum computing Quantum computing utilizes the laws of physics such as quantum mechanics to perform calculations significantly faster than classical computers. This opens up the door for thousands of potential applications such as financial modeling, optimization, quantum communication, and (which I will be diving further into later on in the article). As technology continues to advance, an increasing amount of computer systems will eventually adopt quantum computing strategies to enable a wide range of applications and solve prevalent problems by applying fundamental physical principles. quantum cryptography In this article, I will provide a brief rundown of quantum circuits and the principles of quantum mechanics that make it all work. Table of Contents Quantum States and Qubits State Vectors and the Bloch Sphere Quantum Circuits Diving Deeper into Quantum Gates Sample Quantum Circuit Quantum Circuit Applications Quantum Cryptography Quantum Key Distribution Mechanics behind QKD Vulnerability Example: RSA BB84 Protocol Conclusion Quantum States and Qubits Before we begin the deep dive into quantum circuitry, let’s uncover what quantum circuits are composed of — quantum gates and qubits. Qubits Classical computers like the one you are probably using right now rely on bits to transport information. A bit represents a state which can only be one of two values 0 and 1. Bits can be pieced together to make binary which can be used to represent any piece of information ranging from numbers to text. For instance, the number 12 is represented by 1100 in binary. This is because binary has a base of 2 meaning its possible value slots can be 2⁰, 2¹, 2³, 2⁴, and so forth. Applying this concept, the conversion of 12 into binary would be as follows… 8 4 2 1
1 1 0 0 -> 8 + 4 + 0 + 0 -> 12 We know that 8 and 4 add up to 12, so 8 and 4 are assigned the value of 1 in binary. On the other hand, 2 and 1 are irrelevant to producing the output of 12, so they are both given the value of 0. This gives us the final binary value of 1100 for the number 12. We can apply the principle of bits to quantum computing to understand qubits. Qubits just like bits are used to represent the states of a computer, however, unlike bits, they can exist in a superposition of states meaning they could represent both 0 and 1 simultaneously. But, how could this be? How can one unit represent both possible values at the same time? Superposition To understand superposition further, let’s use an analogy. Imagine flipping a coin up into the air. While the coin is in the air, it is in a state of superposition where the coin represents the values of heads and tails simultaneously. We can only definitively know the value of the coin when it falls to the ground and its state of superposition collapses resulting in either heads or tails. Similarly, in quantum mechanics, a qubit (aka a quantum bit) can exist in a state where it represents both the values of 0 and 1. We can only tell the definite state of the qubit once it collapses into 0 or 1. Quantum Gates Quantum gates are very similar to logic gates in a circuit. A logic gate, which applies both to classical and quantum computers, is a structure that takes in a binary input (ie. 0 and 1, spin-up electrons and spin-down electronics, cats and dogs, etc) and results in a single value (ie. 1, spin-up electron, and a dog) using a system called a boolean function. These gates can then be used together to produce robust circuits. The difference between classical gates and quantum gates emerges once you introduce qubits. What differentiates quantum gates and classical gates are superposition, reversibility, and entanglement. Unlike classical gates that do not have quantum mechanics at their disposal, quantum gates can retain information about what values pass through them making them inherently reversible. Everything passed through a quantum gate can be reversed, however, the same principle is not upheld for classical gates. In short, a quantum gate is used to manipulate inputs into specific desired outputs. Entanglement One last important concept to learn is entanglement. Quantum entanglement occurs when the states of two or more particles become interlinked and interdependent. This allows researchers to determine the state of one particle by measuring the state of the other particle, regardless of the distance that separates them. For instance, if spin-up and spin-down particles are both present, you can accurately infer the configuration of the spin-down particle by referencing the state of the spin-up particle. Entanglement becomes important once we being looking further into quantum algorithms especially those that rely on secure communication channels. State Vectors and the Bloch Sphere Vector Theory Before taking a deeper dive into state representation with vectors, it is important to understand some preliminary knowledge of vectors. State Vectors In quantum physics, state vectors are used the describe the current state of the system. A state vector hosts a collection of numbers within a vector where each element in the vector contains the probability of the qubit being in a specific state. A simple example is as follows… The image above illustrated a qubit |0⟩ that definitively outputs the 0 when it is measured. Likewise, there must also be a qubit that outputs 1 shown by |1⟩. We know these two states are mutually exclusive because there is no convergence between the states (the qubit outputs a 0 or a 1). This is represented above by orthogonal vectors. We can apply the same concept to a slightly more complicated example below… The image above (denoted by |q0⟩) describes a more nuanced state than just |0⟩ and |1⟩. The qubit above can be rewritten as the following: This state demonstrates a state vector for the qubit q0 where the output is not entirely |0⟩ or |1⟩ rather the output is a linear combination of the two also known as a superposition. Bloch Sphere In order to visualize the abstract phenomena of a superposition, quantum physicists use a mathematical tool called the Bloch Sphere to visualize the possible states of a qubit. Any point on the Bloch Sphere can be a possible state for a qubit. The below image visualizes a qubit in the state of |+⟩ where theta = pi / 2 and phi = 0 Quantum Circuits To demonstrate qubits and quantum gates in action, we can look at a circuit diagram where the inputs are shown to the left and the outputs on the right. The operations in between are gates represented by obscure symbols. This is a typical circuit for a standard bit-based computer. The input signals are A, B, and C which are all passed into the circuit and manipulated by the gates in between to produce the resultant signal Q. This is a classical circuit diagram visualizing a classical circuit. Quantum circuit diagrams elevate this circuit convention a bit further because they also have to take into account their inherent reversibility. Therefore, they look a bit different and they follow a few different rules from classical circuits. Here is what a quantum circuit looks like… Let’s break this circuit down into its components: Each horizontal line (ie. q0, q1, and q2) represent a single qubit The left-most letters denote the name of each qubit (ie. q0, q1, and q2) The diagram is split into columns where each column represents a step in the sequence of the algorithm. You can think of them as steps in sequential moments of time (like notes in music) The blocks in between represent quantum gates that perform operations on qubits The key difference between the quantum circuit and the classical circuit is that the quantum circuit shows qubits displayed in straight horizontal lines whereas whereas classical circuits have bit lines that can go in various directions. quantum gates have the same amount of input and output qubits Diving Deeper into Quantum Gates Pauli X-gate The Pauli X-gate provides a simple introduction to the inner workings of quantum gates. Its purpose is very simple: negation. Very similar to the classical NOT gate, the Pauli X-gate flips the state of the qubit to its opposite value which is outlined by the truth table below. Applied to the physical world, the function of the X-gate turns the spin-up state of an electron into a spin-down state and vice versa. → OR → |0> |1> |1> |0> Pauli Y-gate and Z-gate Similarly to the X-gate, the Pauli Y-gate is a single qubit operation that converts: |0＞ → -i|1＞ AND |1＞ → i|0＞ This can be shown by rotating the qubit around the Y-axis on the Bloch sphere. The Pauli Z-gate is also a single qubit operation that maps and does not change |0＞. The Z-gate operation can be visualized by a rotation about the z-axis of the Bloch sphere by pi radians. |1＞ → -|1＞ Bloch sphere representations of Pauli X, Y, and Z gates Hadamard Gate The Hadamard gate is the most ubiquitous gate used in quantum computing. It is a single qubit operation that results in the following mapping: ∣0＞ → (|0＞ + |1＞)/√2 AND ∣1＞ → ( |0＞ — |1＞)/√2 This creates an equal superposition of the two basic qubit states meaning that the state will have an equal probability of being 1 or 0. Controlled Gates A controlled gate is a 2+ qubit operation where more than one qubit can act as a control for some operation on a qubit. For example, CX, CY, and CZ gates. Controlled X Gate The controlled x-gate also known as the CX gate acts on 2 qubits and performs the NOT operations on the second qubit when the first qubit state is |1＞. Other Gates There are lots more gates that are beyond the scope of what this article can cover. If you want to take a deeper look into other quantum gates and their functionalities, I would recommend looking into this summary of quantum operations and gates by Qiskit. Example of a Quantum Circuit Let’s take our knowledge of quantum circuits further by applying it to analyze this example circuit below… This example circuit above utilizes two Hadamard gates and a CNOT (aka CX) gate in order to create an entangled state. Initially, applying the two Hadamard gates creates a superposition state, then we apply the CX gate. The initial state of the qubit is |0⟩. When we pass that state through a Hadamard gate, the output it |+⟩. Since we have two Hadamard gates in this circuit, we get the tensor product of the two values which is |+⟩ ⊗ |+⟩. This results in the output of |00⟩ + |01⟩ + |10⟩ + |11⟩ because applying the CX gate does not do anything in this case. After operating this circuit, the output should be 4 stages with equal probabilities. If we visualize the state vector for each qubit using the Bloch Sphere, we can see that applying the Hadamard gate to each of the qubits resulted in a switch from the Z(|0⟩, |1⟩) to the X basis, and the CX gate did not alter anything as shown by the image below… Quantum Circuit Applications Quantum circuits have a wide range of applications including quantum simulations which simulate the behavior of quantum systems like modeling the behavior of materials with unique properties, solving complex optimization problems in a wide range of fields from finance to logistics, and machine learning algorithms such as those used for image and audio recognition, and cybersecurity applications like cryptography. Quantum Cryptography We can now apply our knowledge of quantum mechanics in order to understand how it can be employed to ensure the security of communication. Quantum cryptography allows a secure channel for communication between two parties by exchanging encryption keys between them to encrypt and decrypt messages. Traditional cryptographic systems that rely on classical computing technologies are founded upon mathematical principles which can be broken and decrypted quickly by powerful computers. Quantum cryptography solves this problem by using the uncertainty principle of quantum mechanics to make it impossible for an eavesdropper to intercept communication between two parties without being detected. Quantum Key Distribution One protocol that implements quantum cryptography is quantum key distribution (aka QKD). It is the most widely studied method of quantum cryptography. QKD utilizes a series of photons to transmit a secret represented by a random sequence known as a key. By doing this, we can detect when a key is compromised by comparing the values at each end of the transmission. With classical computation systems such as a telephone line, it is possible to intercept a secret code by “listening in”. However, there is no possible way to do this with QKD because any attempt to observe a quantum encrypted key will disrupt the photons passing through the transmission leading to a different value at the end. Let’s refer to the image below to demonstrate this phenomenon. Alice and Bob both want to exchange a secret key with each other, but they also want to avoid Eve from snooping into the messages they send each other. By using QKD, Alice is able to send secure messages to Bob by transmitting photons in specific quantum states. Bob is able to decrypt the message by measuring each of the photons sent by Alice to decipher their state. The uncertainty principle tells us that any kind of measurement performed on a quantum state of a particle will indefinitely alter its state. In QKD, this is used as a protective mechanism because if Eve tries to measure the quantum particles sent by Alice to Bob, the particle states will end up being altered making her presence known. Moreover, if Eve tries to covertly copy the particles sent across during the transmission, she would be unable to thanks to the . This system provides a way for both Alice and Bob to transmit secret keys also known as a one-time pad without the overbearing paranoia that Eve might be snooping in on their messages. “no-cloning theorem” QKD supports many different protocols. Some include which utilizes one photon to transmit information between Alice and Bob, which uses weak laser pulses to send photon states, and which sends pairs of entangled photon sources to Alice and Bob. single Photon QKD Weak-Coherent Laser Pulse QKD Entangled Photon QKD Mechanics behind QKD The physical quantum channel in which QKD takes place uses a variety of physical systems like photons, ions, or superconducting circuits. For photon-based QKD, the circuit usually uses a source of single photons, a beam splitter, two polarization filters, and two single-photon detectors. The circuit is constructed so that when Alice sends a sequence of single photons to Bob each with a random polarization state, Bob randomly chooses between the two polarity states to measure the photons which determine the final key bit value. Once all the photons have been measured, Alice and Bob can compare the subset of each of their results using a classical channel to detect any eavesdropping attempts. Vulnerability Example: RSA The RSA (aka Rivest–Shamir–Adleman) cryptographic algorithm is a widely used protocol to secure communication over public channels. However, quantum computers introduced an inherent vulnerability in the algorithm which was that it depended on the inefficiency of classical computing systems to factor large numbers. In response to this vulnerability, security researchers have developed multiple quantum-resistant algorithms such as Quantum-Safe RSA (aka QS-RSA). QS-RSA is a modified version of RSA that uses an alternative quantum-safe mathematical function to generate public and private keys while retaining the traditional RSA encryption/decryption functionalities. BB84 Protocol Another quantum-safe cryptographic protocol is BB84. For the BB84 Protocol, we define the binary 0 to be configured on 0° on the rectilinear basis or 45° on the diagonal basis. Similarly, binary 1 is represented by 90° on the rectilinear basis or 135° on the diagonal basis. Firstly, Alice sends over information through the quantum channel by randomly selecting a string of bits and bases (either rectilinear or diagonal) of equal length. Subsequently, each bit of the string will be iterated and Alice transmits a photon of the same polarization through the channel to Bob. Once Bob receives the photons, he randomly chooses a basis for each photon to measure its polarity. If the polarity he chooses matches the one sent by Alice, he will correctly find the bit Alice sent. If the bit does not match the one that Alice sent, Bob will be assigned a random bit. Secondly, Alice and Bob communicate over a public channel to communicate the bases Bob used to measure the photons sent by Alice. Then, Alice sends back to Bob the bases Bob was able to guess correctly for the encoded bits. Afterward, Alice and Bob both remove the encoded and measured bits on different bases resulting in an identical bit-string called the . shifted key In order to check if anyone is snooping in on the information transmission (ahem Eve), Alice and Bob can exchange a few bits of the shifted key that are supposed to match. If any of the exchanged bits do not match, we can ensure that Eve was listening in on the transmission. The below image shows an example where Eve was monitoring the transmission between Bob and Alice. Even though Alice and Bob both had six matching bases, only one of those bases match revealing the presence of Eve. This will cause Alice and Bob to revert to another Quantum Channel to continue their communications. Conclusion Overall, this article provides a small glimpse into the up-and-coming technologies behind quantum computing. I hope the examples and descriptions provided helped to elucidate a few concepts of quantum mechanics: quantum circuity, entanglement, superposition, quantum gates like the Pauli Y-gate, and quantum cryptography algorithms and protocols like QS-RSA and BB84. Quantum mechanics has revolutionized cryptography by providing more effective methods to secure communication channels. It is clear that applying quantum mechanics to cybersecurity-specific applications will continue to transform the ways we communicate and secure our data for years to come. Also published here.

Enhancing Cryptography with Quantum Circuits & Key Distribution

কোয়ান্টাম সার্কিট এবং কী ডিস্ট্রিবিউশন সহ ক্রিপ্টোগ্রাফি উন্নত করা

Verbesserung der Kryptographie mit Quantenschaltkreisen und Schlüsselverteilung

Mejora de la criptografía con circuitos cuánticos y distribución de claves

Améliorer la cryptographie avec des circuits quantiques et la distribution de clés

क्वांटम सर्किट और कुंजी वितरण के साथ क्रिप्टोग्राफी को बढ़ाना

量子回路と鍵配布による暗号化の強化

양자 회로 및 키 분배를 통한 암호화 강화

Aprimorando a criptografia com circuitos quânticos e distribuição de chaves

Kuantum Devreleri ve Anahtar Dağıtımı ile Kriptografiyi Geliştirme

Tăng cường mã hóa với mạch lượng tử & phân phối khóa

使用量子电路和密钥分发增强密码学

Mobile Price Classification: An Open Source Data Science Project with Dagshub

Machine learning models are usually developed in a training environment (online or offline) and then can be deployed to be used with live data.  If you're working in Data Science and Machine learning projects, knowing how to deploy a model is one of the most important skills you'll need to have. Who is this article for? This article is for those who have created a machine learning model in a local machine and want to deploy and test the model within a short time. It's also for those who are looking for an alternative platform to deploy their machine learning models. Let's get started! 🚀 What does it mean to deploy a Machine Learning model? Model deployment is the process of integrating your model into an existing production environment. The model will receive input and predict an output. Machine learning models can be deployed in different environments and can be integrated with different web or mobile applications through an API. “Only when a model is fully integrated with the business systems, we can extract real value from its predictions”. — Christopher Samiullah There are different platforms that can help you deploy your machine learning model. But for most of these platforms, it takes a lot of time and resources to configure the environment and deploy your model. For example, Sagemaker offers a popular library and ML frameworks, but you still have to depend on them for new releases. This might mean that you won't be able to deploy your model on time. Let's say the Sagemaker platform has scikit-learn v0.24 in their environment and you want to train and deploy your model with scikit-learn v1.0.1. You will not be able to do it until Sagemaker upgrades to the new version of scikit learn (1.0.1). In this article, you will learn how to use Aibro to deploy your model quickly and easily. What is Aibro? is a serverless MLOps tool that makes Machine Learning cloud computing cheap, easy, and fast. The tool can help data scientists or machine learning engineers train and deploy machine learning models on cloud platforms within a short period of time. Aibro It currently supports AWS cloud platform and they are planning to support more cloud platforms like Google Cloud, Microsoft Azure, Alibaba Cloud and IBM Cloud. It also supports most of the popular machine learning frameworks in the market like TensorFlow, Pytorch, Scikit-learn, and XGboost. Another advantage of using Aibro is the ability to reduce cloud costs by 85% using an exclusive cost-saving strategy built for machine learning. After understanding Aibro and its services, let's create a simple model and then deploy it. Create a Simple Model The first step is to build the model. We are going to use the to build a model that can classify if a movie review is positive or negative. Here are the steps you should follow to do that. IMDB Movie dataset Import the Important packages We need to import Python packages to load the data, clean the data, create a machine learning model, and save the model for deployment. accuracy_score,
    classification_report,
    plot_confusion_matrix,
) ):
    nltk.download(dependency) # import important modules import numpy as np import pandas as pd # sklearn modules from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.naive_bayes import MultinomialNB # classifier from sklearn.metrics import ( from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer # text preprocessing modules from string import punctuation # text preprocessing modules from nltk.tokenize import word_tokenize import nltk from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer import re #regular expression # Download dependency for dependency in ( "brown" , "names" , "wordnet" , "averaged_perceptron_tagger" , "universal_tagset" , import warnings warnings.filterwarnings( "ignore" ) # seeding np.random.seed( 123 ) Load the dataset from the data folder: # load data data = pd.read_csv( "../data/labeledTrainData.tsv" , sep= '\t' ) And then show a sample of the dataset: data.head() # show top five rows of data Our dataset has 3 columns: Id — This is the id of the review Sentiment — either positive (1) or negative (0) Review — comment about the movie Next, let's check the shape of the dataset: data.shape # check the shape of the data (25000, 3) The dataset has 25,000 reviews. Now we need to check if the dataset has any missing values: # check missing values in data data.isnull(). sum () id 0 sentiment 0 review 0 dtype: int64 The output shows that our dataset does not have any missing values. How to Evaluate Class Distribution We can use the value_counts() method from the Pandas package to evaluate the class distribution from our dataset. data.sentiment.value_counts() # evalute news sentiment distribution 1 12500 0 12500 Name: sentiment, dtype: int64 In this dataset, we have an equal number of positive and negative reviews. How to Process the Data After analyzing the dataset, the next step is to preprocess the dataset into the right format before creating our machine learning model. The reviews in this dataset contain a lot of unnecessary words and characters that we don’t need when creating a machine learning model. We will clean the messages by removing stopwords, numbers, and punctuation. Then we will convert each word into its base form by using the lemmatization process in the NLTK package. The text_cleaning() function will handle all necessary steps to clean our dataset. text = text.split() text = text.split()
        lemmatizer = WordNetLemmatizer() stop_words =  stopwords.words( 'english' ) def text_cleaning ( text, remove_stop_words= True , lemmatize_words= True ): # Clean the text, with the option to remove stop_words and to lemmatize words # Clean the text text = re.sub( r"[^A-Za-z0-9]" , " " , text) text = re.sub( r"\'s" , " " , text) text =  re.sub( r'http\S+' , ' link ' , text) text = re.sub( r'\b\d+(?:\.\d+)?\s+' , '' , text) # remove numbers # Remove punctuation from text text = '' .join([c for c in text if c not in punctuation]) # Optionally, remove stop words if remove_stop_words: text = [w for w in text if not w in stop_words] text = " " .join(text) # Optionally, shorten words to their stems if lemmatize_words: lemmatized_words = [lemmatizer.lemmatize(word) for word in text] text = " " .join(lemmatized_words) # Return a list of words return (text) Now we can clean our dataset by using the text_cleaning() function: #clean the review data[ "cleaned_review" ] = data[ "review" ].apply(text_cleaning) Then split the data into features and target variables like this: y = data.sentiment.values #split features and target from  data X = data[ "cleaned_review" ] Our feature for training is the cleaned_review variable and the target is the sentiment variable. We then split our dataset into train and test data. The test size is 15% of the entire dataset. X_train, X_valid, y_train, y_valid = train_test_split(
    X,
    y, stratify=y,
) # split data into train and validate test_size= 0.15 , random_state= 42 , shuffle= True , How to Create a Model We will train the Multinomial algorithm to classify if a review is positive or negative. This is one of the most common algorithms used for text classification. Naive Bayes But before training the model, we need to transform our cleaned reviews into numerical values so that the model can understand the data. In this case, we will use the . TfidfVectorizer will help us to convert a collection of text documents to a matrix of TF-IDF features. TfidfVectorizer method from scikit-learn To apply this series of steps (pre-processing and training), we will use a from scikit-learn that sequentially applies a list of transforms and a final estimator. Pipeline class sentiment_classifier = Pipeline(steps=[ ]) # Create a classifier in pipeline ( 'pre_processing' ,TfidfVectorizer(lowercase= False )), ( 'naive_bayes' ,MultinomialNB()) Then we train our classifier like this: sentiment_classifier.fit(X_train,y_train) # train the sentiment classifier We then create a prediction from the validation set: y_preds = sentiment_classifier.predict(X_valid) # test model performance on valid data The model’s performance will be evaluated by using the accuracy_score evaluation metric. We use accuracy_score because we have an equal number of classes in the sentiment variable. accuracy_score(y_valid,y_preds) 0.8629 The accuracy of our model is around 86.29% which is good performance. How to Save the Model Pipeline We can save the model pipeline in the model’s directory by using the joblib Python package. #save model import joblib joblib.dump(sentiment_classifier, '../models/sentiment_model_pipeline.pkl' ) Now that we've built our model, let's learn how to deploy it with Aibro. Deployment WorkFlow – a Step by step Guide To deploy your model with Aibro, you need to prepare your model in the properly formatted machine learning model repository. You can quickly take a look at this repository , but we will build the same for the model we have created. https://github.com/AIpaca-Inc/Aibro-examples As you can see from the image of the deployment flow, Aibro will create an inference API from the formatted machine learning model repository, and you will receive a unique API URL and start to make a prediction from your model. All you need to do is to follow these steps. Step 1: Install the aibro Python library To install aibro, run the following command in your terminal: pip install aibro Step 2: Prepare the Model Repository The model repository will be formatted in the following structure. This folder will contain the model you have created. (a) model folder The data folder will have a JSON file that has an input value. For our case, the input will have a text value (review) as follows. (b) data folder { "data": "I loved it, the kids loved it. It shows them that anything is possible but more especially when you have that one person fighting for you. That one person who believes in you without fail. I appreciated the various life lessons included in the film about being humble and thankful but commanding respect at the same time despite where or what background you come from. Success doesn’t see age, race or gender but sadly opportunity often does. Will Smith doesn’t let the lack of opportunity beat them as a family and the family is a team. The bigger picture is always knowing that there is a team involved in most successful people." } Remember there is no restriction on how you want to format your input and output. Note: The python file should contain two python functions. (c) predict.py This function is responsible for loading the machine learning model from the model folder and returning it. In this tutorial, we will use the joblib package to load the model we have created. load_model(): def load_model (): #load model model = joblib.load( "model/sentiment_model_pipeline.pkl" ) return model This function will receive a model as the input and then load the data from the data folder. Finally, it will make predictions and return the result. run(): data = json.load(fp) def run ( model ): fp = open ( "data/data.json" , "r" ) review = text_cleaning(data[ "data" ]) result = { "data" : model.predict([review])} return result Therefore the predict.py will look as follows: data = json.load(fp) run(load_model()) # import important modules import json # load data import joblib # load model from clean import text_cleaning # function to clean the text def load_model (): #load model model = joblib.load( "model/sentiment_model_pipeline.pkl" ) return model def run ( model ): fp = open ( "data/data.json" , "r" ) review = text_cleaning(data[ "data" ]) result = { "data" : model.predict([review])} return result if __name__ == "__main__" : Aibro will first need to install the packages required to run your model before deploying the model itself. You can either manually write the packages and their version number in the requirements.txt or run the following command which will do the same: (d) requirements.txt pip list -- format =freeze > requirements.txt nltk==3.6.7 numpy==1.19.1 pandas==1.0.5 scikit_learn==0.23.1 joblib==1.0.0 It is also recommended to use the pipreqs Python package to generate requirements.txt. This is because it will include Python packages based on imports in your project instead of all packages in your environment. Note: $ pipreqs /home/aibro_project Successfully saved requirements file /home/aibro_project/requirements.txt You can also include other files or folders that will be used by the predict.py Python file. For example, in the model we have created, we will need to clean the input before making a prediction. (e) Other Artifacts The clean.py contains a Python function that will clean the text before making a prediction. nltk.download(dependency) nltk.download(dependency) nltk.download(dependency) text = text.split() text = text.split()
       lemmatizer = WordNetLemmatizer() # import packages import nltk # Download dependency corpora_list = [ "stopwords" , "names" , "brown" , "wordnet" ] for dependency in corpora_list: try : nltk.data.find( 'corpora/{}' . format (dependency)) except LookupError: taggers_list = [ "averaged_perceptron_tagger" , "universal_tagset" ] for dependency in taggers_list: try : nltk.data.find( 'taggers/{}' . format (dependency)) except LookupError: tokenizers_list = [ "punkt" ] for dependency in tokenizers_list: try : nltk.data.find( 'tokenizers/{}' . format (dependency)) except LookupError: from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer from nltk.tokenize import word_tokenize import re #regular expression from string import punctuation stop_words =  stopwords.words( 'english' ) # function to clean the text def text_cleaning ( text, remove_stop_words= True , lemmatize_words= True ): # Clean the text, with the option to remove stop_words and to lemmatize word # Clean the text text = re.sub( r"[^A-Za-z0-9]" , " " , text) text = re.sub( r"\'s" , " " , text) text =  re.sub( r'http\S+' , ' link ' , text) text = re.sub( r'\b\d+(?:\.\d+)?\s+' , '' , text) # remove numbers # Remove punctuation from text text = '' .join([c for c in text if c not in punctuation]) # Optionally, remove stop words if remove_stop_words: text = [w for w in text if not w in stop_words] text = " " .join(text) # Optionally, shorten words to their stems if lemmatize_words: lemmatized_words = [lemmatizer.lemmatize(word) for word in text] text = " " .join(lemmatized_words) # Return a list of words return (text) How to Test the Repo with Dryrun Before we deploy our model, we can test the repo using Dryrun. Dryrun will locally validate the repo structure and test if the inference result can be successfully returned. The following line of code will test the repo we have created: api_url = Inference.deploy( ) from aibro import Inference artifacts_path= "./sentiment_model_repo" , dryrun= True , The formatted model repository is saved at path “./sentiment_model_repo”. Note: The result shows that the prediction finished without errors. Now we can deploy the model. How to Create an Inference API with One Line of Code To deploy the model, you need to configure the following variables in the inference.deploy() method. The model name should be unique with respect to all current active inference jobs under . In this example, the model name will be "my_sentiment_classifier". (a) model_name your profile This is the id of the machine that will run our model. For this example we will use "c5.large.od".You can see the entire list in the . (b) machine_id_config marketplace This will be the path to your formatted machine learning model repository. For this example, the path is . (c) artifacts_path "./sentiment_model_repo" You can also add a description of your model deployment. (d) description Finally, the one-line code to create an inference API will look as follows. api_url = Inference.deploy( ) from aibro import Inference model_name = "my_movie_sentiment_classifier" , machine_id_config = "c5.large.od" , artifacts_path = "./sentiment_model_repo" , description= "my first inference job" , Once the deployment is finished, an API URL is returned with the syntax like this “ ” http://api.aipaca.ai/v1/{username}/{client_id}/{model_name}/predict if your inference job is public, {client_id} is filled out with "public". Otherwise, {client_id} should be filled out with one of your ' IDs. Note: clients In this tutorial, the API URL will be http://api.aipaca.ai/v1/DavisDavid/public/my_sentiment_classifier/predict How to Test an Aibro API We successfully deployed the model and got the API URL. Let’s test the model and see the result. We will use a Python package called requests to send a request to the API URL and get results. The posted data will replace everything in the data folder. Therefore, your posted data should have the same format as whatever you had in the data folder initially. Note: prediction = requests.post( data=review,
)
 
result = prediction.text import requests import json review = { "data" : "A truly beautiful film that will having you crying with joy and pride. The (few) poor reviews cite a lack of authenticity regarding Richards character and a lack of screen time for the other major family members, including Serena. While I admittedly don’t know exactly the kind of person and father Richard was" } "http://api.aipaca.ai/v1/DavisDavid/public/my_movie_sentiment_classifier/predict" , print (result) The prediction result is {‘data’: array([1])}. As you can see we managed to predict by using the API, and the model predicts that the review is . positive (1) Complete the inference job If you're no longer going to use your inference job, you should shut down the API to avoid unnecessary costs. You can shut it down by passing the inference job id in the Inference.complete() method. rom aibro.inference import Inference id = "inf_cd712f4a-4b59-4e44-8787-9c5b5450ff6d" Inference.complete(job_id= id ) You will receive an output that the inference job successfully completed. Final Thoughts In this article, you have learned the fastest and simplest way to deploy a Machine Learning model to the cloud by using Aibro. You don't need to take a lot of your time and resources to configure the environment – just install aibro and you are good to go. There is a free community edition you can use for your small ML projects. But Alpaca is also giving free credits to new users so you don't need to worry about the cloud costs. There are a lot of features from Aibro that you can use while deploying your model. To learn more you can visit our beautiful designed documentation pages and you can also join our community to get more help. here here You can download the source code used in this article here: https://github.com/Davisy/Aibro-ML-Model-Deployment If you learned something new or enjoyed reading this article, please share it so that others can see it.

How to Deploy Machine Learning Models to the Cloud Quickly and Easily

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Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance. To help you understand this topic I will give answers to some relevant questions about machine learning. But before we answer these questions, it is important to first know about the history of machine learning. A Brief History of Machine Learning You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”. In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the . Perceptron Model In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines. In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition. Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information. During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning. Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning? What is Machine Learning? Have you ever wondered how Facebook’s feature always provides you with a genuine list of people that you actually know in real life and with whom you should connect with on Facebook as well? How does Facebook come to know about this? How are they doing this recommendation? People you may know Well, Machine Learning is an answer to this question. The Definition of Machine learning according to Tom Mitchell: “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience” In simpler words, machine learning is the field of computer science which makes the machine capable of learning on its own without being explicitly programmed. The point to be noted here is that ML algorithms can learn on its own from past experiences, just like humans do. When exposed to new data, these algorithms learn, change and grow by themselves without you needing to change the code every single time. So basically, what happens is that, instead of you writing the code every single time for a new problem, you simply feed the data to the ml algorithm and the algorithm/machine builds the logic and provides results based on the given data. Initially, the results obtained might not be of high accuracy but, over time the accuracy of ml algorithms become higher as it continuously performs tasks. How do Machine Learning algorithms work? utilize a variety of techniques to handle large amounts of complex data to make decisions. These algorithms complete the task of learning from data with specific inputs given to the machine. Machine Learning algorithms It’s important to understand how these algorithms and a machine learning system as a whole work, so that we can get to know how these can be used in the future. It all starts with training the machine learning algorithm by using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction. The predictions and results are evaluated for accuracy. If the prediction is not as expected, the algorithm is re-trained again and again until the desired output is obtained. This enables the ml algorithm to learn on its own and produce an optimal answer that will gradually increase in accuracy over time. After a desired level of accuracy is obtained, the machine learning algorithm is deployed. Google's Image Classification Let me explain to you how machine learning works with a simple example: When you search for “Lion images” on Google Search (as seen in the image below), Google is incredibly good at bringing relevant results, but how does Google achieve this task? Google first gets a large number of examples(datasets) of photos labelled “LION”. Then the Machine learning algorithm looks for patterns of pixels and patterns of colours that will help it predict if the image is of “LION”. At first, Google’s computers make a random guess of what patterns are good in order to identify an image of a LION. If it makes a mistake, then a set of adjustments are made in order for the algorithm to get it right. In the end, such collection of patterns will be learned by a large computer system modeled after the human brain, that once trained can correctly identify and bring accurate results of LION images on Google. If you were in charge of building a machine-learning algorithm to try and identify images between lions and tigers. How will you go about it? The first step as I explained above would be to gather a large number of labelled images with “LION” for lions and “TIGER” for tigers. After this, we will train the computer to look for patterns on the images in order to identify lions and tigers respectively. Once the machine learning model has been trained, we can give it  different images to see if it can correctly identify lions and tigers separately. As seen in the image above, a trained machine learning model can correctly identify such queries. Now that we know how machine learning algorithm works, we should dive a bit deeper into this topic and explore various types of machine learning. Types of Machine Learning Machine Learning is broadly divided into three main areas, supervised learning, unsupervised and reinforcement learning. Each one of these has a specific action and purpose, yielding particular results by using various types of data. Supervised Machine Learning Supervised learning in simple language means training the machine learning model just like a coach trains a batsman. In Supervised Learning, the machine learns under the guidance of labelled data i.e. known data. This known data is fed to the machine learning model and is used to train it. Once the model is trained with a known set of data, you can go ahead and feed unknown data to the model to get a new response. Unsupervised Machine Learning Unsupervised machine learning in simple language means the ML model is self-sufficient in learning on its own. In unsupervised machine learning, there is no such provision of labelled data. The training data is unknown or unlabeled. This unknown data is fed to the machine learning model and is used to train the model. The model tries to find patterns and relationships in the dataset by creating clusters in it. The thing to be noted here is that unsupervised learning is not able to add labels to the clusters. For example, it cannot say this is a group of oranges or mangoes, but it will separate all the oranges from mangoes. Reinforcement Learning In , the machine learns from a hit and trial method. Whenever the model predicts or produces a result, it is penalised if the prediction is wrong or rewarded if the prediction is correct. Based on these actions the model trains itself. reinforcement learning After understanding the basic concepts and types of Machine learning, I think now we are in the right position to understand its importance and its applications. Why is Machine learning Important? - Andrew Ng “Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” I think most of you will agree with this. It is quite hard to think of any industrial activity the wouldn't benefit from the use of machine learning or artificial intelligence. Machine learning is important because of its wide range of applications and its incredible ability to adapt and provide solutions to complex problems efficiently, effectively and quickly. To better understand the importance of machine learning let me go ahead and list certain instances where machine learning is applied. Applications of Machine learning Machine learning is everywhere. Because of a wide range of applications of machine learning, it is possible that you might be using it in one way or the other and you don’t even know about it. Below I will be listing a few applications of machine learning. : Siri, Alexa, Google some of the common examples of virtual personal assistants. These assist in finding information when asked over voice. While answering your query, these personal assistants’ lookout for information recalls your related queries or sends a command to other resources in order to collect information. Machine learning is an integral part of the functioning of personal assistants as they collect and refine the information on the basis of your previous queries. Later this refined dataset is used to give results that are tailored to your preferences. Virtual Personal Assistant : You simply look at your phone and the phone unlocks. The camera in your phone recognizes unique features and projections on your face using image processing (part of machine learning) in order to identify that the person unlocking the phone is not someone else but you. The entire process at the back end complicated but seems to be a simple application of ML at the front-end. Facial recognition : How does your mailbox automatically identify if the email you received is spam or not? Well, again here ML is to thank for. The email spam filter uses supervised machine learning model to filter out spammy emails from your mailbox. Email spam filter 4. : Have you ever wondered how Amazon or Flipkart shows relevant products after you make a purchase from their platform. This is the magic of ML. Recommendation engine on an e-commerce website Once a user buys something from an e-commerce website it stores the purchase data for future reference and finds products that are most likely to be bought by the user in future. This is possible because of machine learning future algorithm model, which can identify patterns in a given dataset. Some other applications of machine learning include: 1. Online fraud detection 2. Social Media Services such as “People you may know” on Facebook, “Similar pins” in Pinterest 3. Online Customer Support i.e. Chatbot 4. Search Engine Result Refining 5. Predictions while commuting using Google Maps I hope this article helped you understand the history of machine learning, what it is, and why it is important. As AI technology improves, we will continue to see machine learning become more and more integrated into the technologies we use in our daily lives.

What is Machine learning and Why is it Important?

Machine learning (ML) and artificial intelligence (AI) technologies can hardly be called emerging in 2019. For the last decade, domains of all sorts have been leveraging them, and speaks to the fact. Today, ML and AI create value for organizations across Consumer Services, Automotive, Agriculture, Retail, Healthcare, and other major industries. the visualization by McKinsey Global Institute As businesses realize these technologies are a boon, the demand for machine learning consulting companies is skyrocketing, which predictably influences the supply. In such a way, today’s IT market is literally larded with ML vendors tackling projects of any scope and budget. To help you make your way through this enormous and diverse offering, we assembled a list of today’s most trustworthy vendors of machine learning consulting and development services. Our IT market analysts assessed an extensive range of companies according to the following objective criteria: Proven machine learning expertise Prior projects: size and complexity Diversity of business domains served Client references Speed and quality of customer service As a result, we got a list of 30 companies worthy of attention. For readers’ convenience, we present the vendors in descending order. 1. Itransition A recognized custom software development company, Itransition has delivered 1,500 projects over the span of its 20-year market presence. With more than 2,000 IT experts on board, the company has served 810 clients from 30 countries. Among the ML services the company provides there are data mining, natural language processing, computer vision, robotic process automation, and machine learning consulting. To deliver its ML solutions, Itransition uses such technologies as Python, R, Azure, Google Cloud, and AWS. Location: Denver, CO Established: 1998 Project budgets start at: $10,000 Key clients: Expedia, Xerox, Toyota, eBay, PayPal, Shell, adidas Other, non-ML services: custom software development, CRM consulting, ecommerce development Website: https://www.itransition.com/ 2. Tooploox EU-based Tooploox has around 100 skilled experts on board. For seven years of the company’s operation, they delivered 150+ successful projects. Tooploox is a machine learning consulting company using this technology to deliver computer and 3D vision, sensor fusion, deep learning, natural language processing, reinforcement learning, and other ML-related services. The company has expertise with other emerging technologies, such as blockchain. Location: Wrocław, Poland Established: 2012 Project budgets start at: $25,000 Key clients: CoinDesk, DTS, ING, Spire Health, Group Nine Other, non-ML services: mobile app development, product design Website: https://www.tooploox.com/ 3. Skim Technologies London-based Skim Technologies is an internationally recognized machine learning consulting company delivering a wide range of related services. The company’s data scientists offer chatbot development, custom ML model creation, and NLP engineering. Skim Technologies also delivers AI workshops as well as ML and AI consulting services. Location: London, United Kingdom Established: 2014 Project budgets start at: $10,000 Key clients: Jones Lang-Lasalle, Breast Cancer Care, Quant Insight Other, non-ML services: business intelligence consulting, custom software development Website: https://www.skimtechnologies.com/ 4. ThirdEye Data US-based ThirdEye Data has been delivering its ML development services for nine years. The company specializes in chatbot development, custom ML algorithms, and machine learning consulting. Among the data science technologies the company works with, there are Spark, TensorFlow, Caffe, and others. Location: Santa Clara, CA Established: 2010 Project budgets start at: $25,000 Key clients: Microsoft, Amazon, Nokia, Ford Direct, Getty Images, Amgen Other, non-ML services: business intelligence consulting, cloud consulting Website: https://thirdeyedata.io/ 5. Chatbots.Studio As its name implies, Chatbots.Studio is a company focusing on AI-powered chatbot development. It builds multichannel conversational platforms, automates business processes, and designs conversations. The company’s technology stack includes React, Redux, Angular 6, Node.js, Express.js, Botkit, and others. Location: Lviv, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Visa, Unibot, Aldi, Digital Flex Other, non-ML services: custom software development Website: https://chatbots.studio/ 6. Azati Software Since 2001, Azati's experts have cooperated with 330 clients globally. Among the ML competencies of the company there are supervised and unsupervised learning, distributed computing, natural language processing, digital signal processing, and machine learning consulting. Azati’s tech toolkit includes CuDNN, Scikit Learn for Python, and Tensor Flow. The company mainly targets clients in the Insurance domain. Location: Livingston, NJ Established: 2001 Project budgets start at: $1,000 Key clients: MedPro Group, SequenceBase, Casio, Thomson Reuters Other, non-ML services: mobile app development, web development Website: https://azati.ai/ 7. Hidden Brains Since its foundation in 2003, Hidden Brains has cooperated with clients from 107 countries. The company delivers AI, ML, deep learning, and data science services. It also specializes in developing natural language processing software. Hidden Brains also delivers chatbots, including those for Facebook, Telegram, WhatsApp, and Slack. Location: Ahmedabad, India Established: 2003 Project budgets start at: $10,000 Key clients: McAfee, Raymond, Maxtor, SanDisk Other, non-ML services: mobile app development, web development Website: https://www.hiddenbrains.com/ 8. InData Labs Established in 2014, InData Labs houses 50+ data scientists, engineers, and analysts. The company develops ML algorithms and natural language processing solutions. Besides, InData Labs specializes in computer vision and predictive modeling. The company's stack includes Python, SpaCy, TensorFlow, Keras, and more proven languages and frameworks. Location: Minsk, Belarus Established: 2014 Project budgets start at: $5,000 Key clients: Captiv8, Flo Health, Wargaming Other, non-ML services: business intelligence consulting Website: https://indatalabs.com/ 9. Arcanys Since 2010, Philippines-based Arcanys has been delivering its ML and AI services to clients from all over the world. The company specializes in sentiment analysis, recommender systems, and image recognition. As one of its cooperation models, the company provides dedicated teams of highly experienced data scientists and developers. Location: Cebu City, Philippines Established: 2010 Project budgets start at: $100,000 Key clients: General Electric Korea, Enfo, Payment Logic, L’Oreal, Spinetix Other, non-ML services: custom software development, mobile app development Website: https://www.arcanys.com/ 10. MobiDev Since 2009, MobiDev’s IT experts have delivered 350+ software solutions. While headquartered in the US, the company provides its services globally. MobiDev uses the following machine learning technologies: Python, R, C++, Go, TensorFlow, ONNX, and Keras. MobiDev’s mobile ML toolkit includes Core ML, TensorFlow Lite, and Cafe2Go. On top of that, the company provides machine learning consulting. Location: Atlanta, GA Established: 2009 Project budgets start at: $10,000 Key clients: Verizon Media, Comcash, Quality Bridge, SmartTab, SMS Group Other, non-ML services: IoT development, web development Website: https://mobidev.biz 11. Spire Digital For over 20 years that Spire Digital has been providing IT services, it has cooperated with 600+ global customers. Among the ML and AI development services the company offers, there are natural language processing, supervised and unsupervised learning, and sentiment analysis. Also, Spire Digital creates chatbots for Facebook Messenger, Slack, Skype, and Cisco WebEx. Location: Denver, CO Established: 1998 Project budgets start at: Undisclosed Key clients: TomTom, Lockheed Martin, US Navy, USDA, Western Union Other, non-ML services: custom software development, mobile app development Website: https://www.spiredigital.com/ 12. Icreon New York-based Icreon is a digital innovation agency mostly targeting medium-sized businesses. The company uses AI and ML technologies to create innovative applications, platforms, and ecosystems. The company’s technological expertise in ML includes Amazon Web Services, Azure, Caffe2, Google Cloud, Keras, and more. Location: New York, NY Established: 2000 Project budgets start at: $25,000 Key clients: National Geographic Channel, FOX Movies, Harry Winston, Nokia Other, non-ML services: custom software development Website: https://www.icreon.us/ 13. Miquido For eight years, Miquido's team has successfully completed more than 100 projects, including in machine learning. The company offers solutions for AI-based personalization, predictive and behavioral analytics, and churn prediction. Miquido also develops chatbots. Recognized by Deloitte as one the fastest growing companies in Europe, Miquido also offers UI/UX design creation and mobile apps development. Location: Kraków, Poland Established: 2011 Project budgets start at: $25,000 Key clients: Skyscanner, Abbey Road Studios, HelloFresh Other, non-ML services: mobile app development, web development Website: https://www.miquido.com/ 14. Neoteriс Founded in 2005, Europe-based Neoteric offers a wide range of ML solutions, including predictive analytics, recommendation engines, NLP, robotic process automation, and machine learning consulting. The company also specializes in such AI platforms as SaaS Manager, SoapBox, AppOrchid, and RapidSOS. Location: Gdańsk, Poland Established: 2005 Project budgets start at: $10,000 Key clients: LunchBadger, Arvata, IT Trans Other, non-ML services: custom software development, web development Website: https://neoteric.eu/ 15. Unicsoft Established in 2005, Unicsoft employs more than 100 specialists in emerging technologies and software development. Within its ML tech stack, the company offers development of predictive and behavioral analytics solutions, neural networks, and recommendation engines, among other ML applications. The company also delivers consulting services, and maintenance of existing ML platforms. Location: Kiev, Ukraine Established: 2005 Project budgets start at: $5,000 Key Clients: Hoodifood, Executive Eye, GOFORIT, Intellica Group Other, non-ML services: blockchain Website: https://unicsoft.com/ 16. Abto Software With 12 years of its market presence, Abto Software’s has worked with 200+ global customers. Specializing in AI and ML, the company offers custom enterprise solutions for advanced and predictive analytics, virtual assistants, recommendation systems, and intelligent business process automation. Abto Software works with the following tools and frameworks, among others: TensorFlow, Keras, Caffe, OpenCV, and AWS. Location: New York, NY Established: 2007 Project budgets start at: Undisclosed Key clients: Just Answer, Skybow, Medikit, Up Great Other, non-ML services: web development, enterprise app modernization Website: https://www.abtosoftware.com/ 17. Accubits Technologies, Inc. US-located Accubits started in 2012 and has already cooperated with 140+ clients. The company’s ML capabilities include development of conversational and classification tools, video, image and text analytics solutions, as well as predictive analytics software. Besides, Accubits offers machine learning consulting. The company’s experts work with NumPy, PHP, Python, SpaCy, Spark, and several other platforms. Location: Vienna, VA Established: 2012 Project budgets start at: $5,000 Key сlients: Dubai Land Department, Smart Dubai, Eduvalue, Landmark Other, non-ML services: blockchain, custom software development Website: https://accubits.com/ 18. BotsCrew Established in 2016, BotsCrew is a global leader in chatbot development, delivering to mid-sized companies and startups. BotsCrew has its own enterprise chatbot platform that is ready to be customized and optimized according to customers' needs. Besides the proprietary platform, the company offers development of voice assistants and IoT bots. Location: London, United Kingdom Established: 2016 Project budgets start at: $10,000 Key clients: Ailira, IVA, ReVA, Dr. Dean Other, non-ML services: None Website: https://botscrew.com/ 19. STX Next Founded in 2005, STX Next is a custom software development company that specializes in streamlining IT solutions with AI and ML technologies. The company’s main technological advantage is in its exceptionally skilled Python developers. For 14 years on the market, STX Next has delivered over 250 successful projects. Location: Poznań, Poland Established: 2005 Project budgets start at: $25,000 Key clients: Seerene, Verve, Hogarth WW, Pixomondo, Unity Technologies Other, non-ML services: web development, custom software development Website: https://stxnext.com/ 20. Dogtown Media California-based Dogtown Media specializes in delivering mobile applications, enhanced with ML and AI technologies. Besides ML-powered mobile apps, the company provides machine learning consulting. For the last eight years, Dogtown Media has brought to life at least 200 applications. Location: Venice Beach, CA Established: 2011 Project budgets start at: $25,000 Key clients: Google, Lexus, United Nations, Citibank, Red Bull, YouTube Other, non-ML services: mobile app development, IoT development Website: https://www.dogtownmedia.com/ 21. Sigmoidal Sigmoidal is a New-York-based consultancy specializing in machine learning and data science, mostly for companies in Finance, Trading, and Pharma. The company integrates these technologies into web and mobile applications and APIs. It also offers solutions for NLP, computer vision, and predictive analytics. Location: New York, NY Established: 2016 Project budgets start at: $1,000 Key clients: Undisclosed Other, non-ML services: business intelligence consulting Website: https://sigmoidal.io/ 22. Cyber Infrastructure Established in 2003, Cyber Infrastructure is a custom software development company with 100+ clients worldwide. Its ML expertise includes development of virtual assistants and chatbots for a variety of domains. Cyber Infrastructure also provides machine learning consulting and employs Python, Java, C++, Lisp, and R as its primary ML and AI stack. Location: San Francisco, CA Established: 2003 Project budgets start at: $5,000 Key clients: Mannatech, Bell Other, non-ML services: custom software development, web development Website: https://www.cisin.com/ 23. Azilen Technologies US-headquartered Azilen Technologies has 250+ IT experts on board distributed across four offices worldwide. They employ ML technologies to deliver customer service, survey, and informational bots. On top of that, the company delivers facial recognition and predictive analytics solutions. Location: West Bloomfield, MI Established: 2009 Project budgets start at: $25,000 Key clients: Samsung, Pizza Hut, Sodexo, ICICI Bank Other, non-ML services: custom software development Website: https://www.azilen.com/ 24. Stermedia Stermedia has been delivering its ML and data science services since 2007. The company’s dedicated teams of data scientists and software architects design ML algorithms and models for extracting knowledge from big data sets. The company also provides NLP solutions and uses such technologies as TensorFlow and Keras, to name just a few. Location: Wroclaw, Poland Established: 2007 Project budgets start at: $10,000 Key Clients: Family Fabric, WorkService, ISOK Other, non-ML services: custom software development, web development Website: https://stermedia.ai/ 25. Belatrix Having five offices with 600+ professionals all around the world, Belatrix is one of the most fast-evolving machine learning consulting companies on the market today. Its machine learning experts have a proven track record of building various solutions, from predictive modeling to communication automation. Location: Mendoza, Argentina Established: 2003 Project budgets start at: $5,000 Key clients: Disney, Adobe, AOL, FIS, Chatman Financial Other, non-ML services: custom software development, mobile app development Website: https://www.belatrixsf.com/ 26. DataRoot Labs DataRoot Labs is an agency focusing on ML and AI development. It delivers full-cycle ML software development, including project evaluation, machine learning consulting, strategy development, architecture, integration, and support. Besides, DataRoot Labs helps enterprises with digital transformation with AI at the core. Location: Kiev, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Servers.com, Everad, ABM Cloud, Cargofy Other, non-ML services: custom software development, mobile app development Website: https://datarootlabs.com/ 27. Softengi Since 1995, Softengi has completed 900+ projects for clients across six continents. The company’s ML services include chatbot engineering and NLP algorithms. For these purposes, Softengi uses R, Hadoop, and Python, among other tools. Location: Kiev, Ukraine Established: 1995 Project budgets start at: $10,000 Key clients: Boeing, Zeppelin, Enviance, OneRoof Energy Other, non-ML services: custom software development, AR/VR development, IoT development Website: http://softengi.com/ 28. Waverley With 27 years of software development experience, Waverley is a company housing 370K talented engineers. Among other services, they offer ML-centric solutions such as data clustering and unsupervised algorithms. On top of that, the company offers NLP and computer vision solutions and chatbots. To deliver ML products, Waverley’s engineers use Java, Python, Scala, and R. Location: Palo Alto, CA Established: 1992 Project budgets start at: $50,000 Key clients: Mozilla, Qualcomm, Swisscom, American Express Other, non-ML services: custom software development, mobile app development, IoT development Website: https://waverleysoftware.com/ 29. Imaginovation Founded in 2011, Imaginovation is a software development agency that has successfully completed 300+ IT projects of various complexity. As for ML and AI, the company develops recommendation and effectiveness assessment engines as well as solutions for speech and text processing automation. Location: Cary, NC Established: 2011 Project budgets start at: $10,000 Key clients: MetLife, Nestle, UNC Pembroke, Contractor Score Other, non-ML services: custom software development, mobile app development, web development Website: https://www.imaginovation.net/ 30. E2logy Established in 2005, E2logy has delivered 300+ projects to businesses of various sizes. Being a leading Amazon Web Services and Azure AI vendor, the company develops ML algorithms and chatbots. They also offer machine learning consulting. In addition to their ML services, E2Logy brings custom IoT solutions to its global customers. Location: Ahmedabad, India Established: 2005 Project budgets start at: $5,000 Key clients: McMillan Publishers, Polaris Industries, Vodafone Other, non-ML services: custom software development, web development Website: https://e2logy.com/ This was a full list of the top 30 machine learning consulting companies in 2019. If you cooperated with any of the presented vendors, please do get in touch and share your story.

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