mloflo

- Written by Shashi Prakash Patel - Written by Shashi Prakash Patel My Introduction: My Introduction: I am Shashi Patel from the consulting sales team. I’ve spent my career in sales and business development, specializing in IT services and staffing solutions. I have a Master’s in Computer Applications (MCA) and along the way I have deepened my understanding of data science and AI through dedicated learning. This technical foundation allows me to connect the dots between AI-driven innovations and real-world business challenges — something I’ve always been passionate about. However, I’ve often felt that my potential is limited by the boundaries of my current role. There’s so much more I can contribute, especially at the intersection of technology and business strategy. I believe that given the opportunity, I could bridge the gap between cutting-edge technology and business impact. That’s what motivated me to step outside my comfort zone and write this blog — something I’ve never done before. It’s my way of showcasing that I’m not just someone who sells tech — I understand it, I’m passionate about it, and I want to play a more active role in shaping its future. This blog is my first step toward broadening my professional scope and sharing my insights with the global tech community. Artificial Intelligence and Machine Learning (AI/ML) are transforming industries, but deploying these models into production remains a complex challenge. Having spent years in IT sales while diving deep into data science and Gen AI concepts, I’ve seen firsthand how streamlining deployment pipelines can make or break a project’s success. In this blog, I’ll explore how MLflow and Kubernetes combine to create a robust, scalable environment for AI/ML model deployment — and why this duo is gaining traction in the tech community. MLflow Kubernetes What is AI/ML Model Deployment with MLflow & Kubernetes? What is AI/ML Model Deployment with MLflow & Kubernetes? 1. AI/ML Model Deployment is the process of taking a trained machine learning model and making it accessible for real-world use — whether that’s predicting customer behavior, optimizing supply chains, or detecting fraud. However, this is more than just pushing code into production. It requires handling: AI/ML Model Deployment Versioning: Ensuring the right model version is deployed.
Scalability: Adapting to fluctuating traffic without performance drops.
Monitoring: Tracking performance to prevent issues like model drift over time. Versioning: Ensuring the right model version is deployed. Versioning Scalability: Adapting to fluctuating traffic without performance drops. Scalability Monitoring: Tracking performance to prevent issues like model drift over time. Monitoring MLflow is an open-source platform that simplifies managing the machine learning lifecycle — from experimentation and tracking to deployment and monitoring. It ensures reproducibility while providing tools to package and deploy the model.
Kubernetes (K8s) is a container orchestration platform that makes deploying models at scale simple and reliable. It manages the infrastructure behind AI deployments, handling tasks like auto-scaling, load balancing, and self-healing. MLflow is an open-source platform that simplifies managing the machine learning lifecycle — from experimentation and tracking to deployment and monitoring. It ensures reproducibility while providing tools to package and deploy the model. MLflow Kubernetes (K8s) is a container orchestration platform that makes deploying models at scale simple and reliable. It manages the infrastructure behind AI deployments, handling tasks like auto-scaling, load balancing, and self-healing. Kubernetes (K8s) Why use them together? Why use them together? MLflow handles the model lifecycle, ensuring every experiment is tracked and reproducible, while Kubernetes takes care of deploying and scaling the models seamlessly. Together, they create a streamlined pipeline where you: Track and package models in MLflow.
Containerize the model (e.g., with Docker).
Deploy and manage the containers using Kubernetes. Track and package models in MLflow. Containerize the model (e.g., with Docker). Deploy and manage the containers using Kubernetes. This combination ensures that models don’t just work in development environments but perform reliably in production at any scale. Why AI/ML Model Deployment is Hard Why AI/ML Model Deployment is Hard The journey from training a model to deploying it at scale presents several challenges: Version Control: Managing multiple models and ensuring the right version is deployed.
Scalability: Handling growing datasets and fluctuating traffic loads.
Reproducibility: Ensuring consistent performance across environments.
Monitoring and Maintenance: Continuously tracking performance and detecting model drift. Version Control: Managing multiple models and ensuring the right version is deployed. Version Control: Scalability: Handling growing datasets and fluctuating traffic loads. Scalability Reproducibility: Ensuring consistent performance across environments. Reproducibility Monitoring and Maintenance: Continuously tracking performance and detecting model drift. Monitoring and Maintenance This is where MLflow and Kubernetes shine, simplifying the deployment process while ensuring operational resilience. MLflow Kubernetes MLflow: Managing the Model Lifecycle MLflow MLflow addresses some of the most critical pain points in the AI/ML lifecycle by offering: MLflow Experiment Tracking: Logs parameters, metrics, and artifacts to track performance across experiments.
Model Packaging: Ensures models are packaged with dependencies for seamless deployment.
Model Registry: Centralizes model versioning and enables smooth collaboration between teams. Experiment Tracking: Logs parameters, metrics, and artifacts to track performance across experiments. Experiment Tracking Model Packaging: Ensures models are packaged with dependencies for seamless deployment. Model Packaging Model Registry: Centralizes model versioning and enables smooth collaboration between teams. Model Registry In essence, MLflow brings structure and traceability to the otherwise chaotic process of building AI models. Kubernetes: Scaling Model Deployment Kubernetes: Scaling Model Deployment Once your model is ready, Kubernetes ensures it performs reliably in production. It automates several key aspects: Kubernetes Auto-scaling: Adjusts resources based on traffic, ensuring performance and cost efficiency.
Portability: Ensures the same deployment process across development, testing, and production.
Resilience: Automatically restarts failed containers, ensuring high availability. Auto-scaling: Adjusts resources based on traffic, ensuring performance and cost efficiency. Auto-scaling: Portability: Ensures the same deployment process across development, testing, and production. Portability: Resilience: Automatically restarts failed containers, ensuring high availability. Resilience By leveraging Kubernetes, AI/ML teams can deploy models once and trust the system to handle scaling and infrastructure management, allowing them to focus on improving the model itself. Kubernetes, AI/ML Why This Matters for Business Why This Matters for Business From a business perspective, adopting MLflow and Kubernetes drives: Faster Time-to-Market: Automating the pipeline reduces deployment cycles.
Operational Resilience: Kubernetes ensures minimal downtime, enhancing reliability.
Cost Efficiency: Auto-scaling optimizes infrastructure costs.
Continuous Innovation: CI/CD pipelines empower rapid experimentation and iteration. Faster Time-to-Market: Automating the pipeline reduces deployment cycles. Faster Time-to-Market Operational Resilience: Kubernetes ensures minimal downtime, enhancing reliability. Operational Resilience Cost Efficiency: Auto-scaling optimizes infrastructure costs. Cost Efficiency Continuous Innovation: CI/CD pipelines empower rapid experimentation and iteration. Continuous Innovation: Conclusion: Driving AI at Scale Conclusion: Driving AI at Scale Deploying AI/ML models isn’t just about getting code into production — it’s about creating scalable, reproducible, and resilient systems that align with business goals. MLflow and Kubernetes provide a powerful combination to simplify model management and ensure reliable performance in production. Deploying AI/ML scalable, reproducible, and resilient systems MLflow Kubernetes As someone passionate about tech’s impact on business, I see these tools as essential for bridging the gap between innovation and real-world impact. ovation and real-world impact This article by Shashi Prakash Patel placed as a runner-up in Round 1 of R Systems Blogbook: Chapter 1. This article by Shashi Prakash Patel placed as a runner-up in Round 1 of R Systems Blogbook: Chapter 1. Shashi Prakash Patel

AI/ML Model Deployment with MLflow & Kubernetes: From Experimentation to Enterprise-Grade Deployment

MLflow & Kubernetes সঙ্গে AI / ML মডেল বিস্তার: পরীক্ষা থেকে এন্টারপ্রাইজ গ্রেড বিস্তার

Implementació de models AI/ML amb MLflow i Kubernetes: de l'experimentació a la implantació de grau empresarial

AI/ML Model Deployment mit MLflow & Kubernetes: Von der Experimentierung bis zur Enterprise-Grade-Deployment

Despliegue de modelos de IA/ML con MLflow y Kubernetes: desde la experimentación hasta la implementación de nivel empresarial

Déploiement de modèles AI/ML avec MLflow et Kubernetes : de l’expérimentation au déploiement en entreprise

Implementación de modelos AI/ML con MLflow e Kubernetes: desde a experimentación ata a implantación de nivel empresarial

MLflow और Kubernetes के साथ एआई / एमएल मॉडल तैनाती: प्रयोग से लेकर एंटरप्राइज़ ग्रेड तैनाती तक

AI/ML modellek telepítése az MLflow és a Kubernetes segítségével: a kísérletezéstől a vállalati szintű telepítésig

MLflowとKubernetesによるAI/MLモデル展開:実験からエンタープライズグレード展開まで

MLflow & Kubernetes와 함께 AI/ML 모델 배포 : 실험에서 엔터프라이즈 그레이드 배포까지

AI / ML Model Deployment na MLflow & Kubernetes: Tango na Enterprise-Grade Deployment

ການນໍາໃຊ້ແບບ AI / ML ກັບ MLflow ແລະ Kubernetes: ຈາກການທົດສອບກັບການນໍາໃຊ້ Enterprise-Grade

Inteligência Artificial/ML Model Deployment with MLflow & Kubernetes: From Experimentation to Enterprise-Grade Deployment

Развертывание моделей AI/ML с помощью MLflow и Kubernetes: от экспериментации до развертывания на уровне предприятия

การใช้งานรุ่น AI / ML ด้วย MLflow & Kubernetes: จากการทดลองไปจนถึงการใช้งานระดับองค์กร

MLflow ve Kubernetes ile AI/ML Model Geliştirme: Deneyimden Enterprise-Grade Geliştirmeye

Phát triển mô hình AI/ML với MLflow & Kubernetes: Từ thử nghiệm đến triển khai cấp doanh nghiệp

使用 MLflow 和 Kubernetes 的 AI/ML 模型部署:从实验到企业级部署

About MLflow MLflow is an open-source platform designed to manage the complete machine learning lifecycle. Databricks created it as an internal project to address challenges faced in their own machine learning development and deployment processes. MLflow was later released as an open-source project in June 2018. As a tool for managing the complete lifecycle, MLflow contains the following components. - An engineer will use this feature the most. It allows experiments to be recorded and queried. It also keeps track of the code, data, configuration and results for each experiment. MLflow Tracking - Allows experiments to be reproduced by packaging the code into a platform agnostic format. MLflow Projects - Deploys machine learning models to an environment where they can be served. MLflow Models - Allows for the storage, annotation, discovery, and management of models in a central repository. MLflow Repositories It is possible to install all these capabilities on a development machine so that engineers can experiment to their heart's content without worrying about messing up a production installation. All the files used to install and setup MLflow can be found in our . Github repository Installation Options The lists no less than 6 options for installing MLFlow. This may seem like overkill, but these options accommodate different preferences for a database and varying levels of network complexity. MLFlow documentation The option that is best suited for an organization that has multiple teams using large datasets and building models that themselves may get quite large is shown below. This option requires the setup of three servers - a Tracking server, a PostgreSQL database, and an S3 Object Store - our implementation will use MinIO. The Tracking Server is a single entry point from an engineer’s development machine for accessing MLflow functionality. (Don’t be fooled by its name - it contains all the components listed above - Tracking, Model, Projects and Repositories.) The Tracking server uses PostgreSQL to store entities. Entities are runs, parameters, metrics, tags, notes, and metadata. (More on runs later.) The Tracking server in our implementation accesses MinIO to store artifacts. Examples of artifacts are models, datasets and configuration files. What is nice about the modern tooling available to engineers these days is that you can emulate a production environment - including tooling choice and network connectivity - using containers. That is what I will show in this post. I will show how to use Docker Compose to install the servers described above as services running in a container. Additionally, the configuration of MLflow is set up such that you can use an existing instance of MinIO if you wish. In this post, I will show how to deploy a brand new instance of MinIO, but the files in our have a `docker-compose` file that shows how to connect to an existing instance of MinIO. Github repository What We Will Install Below is a list of everything that needs to be installed. This list includes servers that will become services in containers (MinIO, Postgres, and MLFlow), as well as the SDKs you will need (MinIO and MLflow). Docker Desktop MLFlow Tracking Server via Docker Compose MinIO Server via Docker Compose PostgresSQL via Docker Compose MLFlow SDK via pip install MinIO SDK via pip install Let’s start with Docker Desktop, which will serve as the host for our Docker Compose services. Docker Desktop You can find the for your operating system on Docker’s site. If you are installing Docker Desktop on a Mac, then you need to know the chip your Mac is using — Apple or Intel. You can determine this by clicking the Apple icon in the upper left corner of your Mac and clicking the “About This Mac” menu option. appropriate installation We are now ready to install our services. MLFlow Server, Postgres and MinIO The MLFLow Tracking Server, PostgresSQL and MinIO will be installed as services using the Docker Compose file shown below. version: "3.3"

services:
  db:
    restart: always
    image: postgres
    container_name: mlflow_db
    expose:
      - "${PG_PORT}"
    networks:
      - backend
    environment:
      - POSTGRES_USER=${PG_USER}
      - POSTGRES_PASSWORD=${PG_PASSWORD}
      - POSTGRES_DATABASE=${PG_DATABASE}
    volumes:
      - ./db_data:/var/lib/postgresql/data/
    healthcheck:
      test: ["CMD", "pg_isready", "-p", "${PG_PORT}", "-U", "${PG_USER}"]
      interval: 5s
      timeout: 5s
      retries: 3
  s3:
    restart: always
    image: minio/minio
    container_name: mlflow_minio
    volumes:
      - ./minio_data:/data
    ports:
      - "${MINIO_PORT}:9000"
      - "${MINIO_CONSOLE_PORT}:9001"
    networks:
      - frontend
      - backend
    environment:
      - MINIO_ROOT_USER=${MINIO_ROOT_USER}
      - MINIO_ROOT_PASSWORD=${MINIO_ROOT_PASSWORD}
      - MINIO_ADDRESS=${MINIO_ADDRESS}
      - MINIO_PORT=${MINIO_PORT}
      - MINIO_STORAGE_USE_HTTPS=${MINIO_STORAGE_USE_HTTPS}
      - MINIO_CONSOLE_ADDRESS=${MINIO_CONSOLE_ADDRESS}
    command: server /data
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"]
      interval: 30s
      timeout: 20s
      retries: 3

  tracking_server:
    restart: always
    build: ./mlflow
    image: mlflow_server
    container_name: mlflow_server
    depends_on:
      - db
    ports:
      - "${MLFLOW_PORT}:5000"
    networks:
      - frontend
      - backend
    environment:
      - AWS_ACCESS_KEY_ID=${MINIO_ACCESS_KEY}
      - AWS_SECRET_ACCESS_KEY=${MINIO_SECRET_ACCESS_KEY}
      - MLFLOW_S3_ENDPOINT_URL=http://s3:${MINIO_PORT}
      - MLFLOW_S3_IGNORE_TLS=true
    command: >
      mlflow server
      --backend-store-uri postgresql://${PG_USER}:${PG_PASSWORD}@db:${PG_PORT}/${PG_DATABASE}
      --host 0.0.0.0
      --serve-artifacts
      --artifacts-destination s3://${MLFLOW_BUCKET_NAME}
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:${MLFLOW_PORT}/"]
      interval: 30s
      timeout: 10s
      retries: 3

  volumes:
    db_data:
    minio_data:

  networks:
    frontend:
      driver: bridge
    backend:
      driver: bridge There are a few things worth noting that will help you troubleshoot problems should something go wrong. First, both MinIO and PostgreSQL are using the local file system to store data. For PostgreSQL, this is the `db_data` folder and for MinIO, it is the `minio_data` folder. If you ever want to start over with a clean installation, then delete these folders. Next, this Docker Compose file is configuration driven. For example, instead of hard coding the PostgreSQL database name to `mlflow`, the name comes from the `config.env` file shown below using the following syntax in the Docker Compose file - `${PG_DATABASE}`. # Postgres configuration
PG_USER=mlflow
PG_PASSWORD=mlflow
PG_DATABASE=mlflow
PG_PORT=5432

# MLflow configuration
MLFLOW_PORT=5000
MLFLOW_BUCKET_NAME=mlflow

# MinIO access keys - these are needed by MLflow
MINIO_ACCESS_KEY=XeAMQQjZY2pTcXWfxh4H
MINIO_SECRET_ACCESS_KEY=wyJ30G38aC2UcyaFjVj2dmXs1bITYkJBcx0FtljZ

# MinIO configuration
MINIO_ROOT_USER: 'minio_user'
MINIO_ROOT_PASSWORD: 'minio_pwd'
MINIO_ADDRESS: ':9000'
MINIO_STORAGE_USE_HTTPS: False
MINIO_CONSOLE_ADDRESS: ':9001'
MINIO_PORT=9000
MINIO_CONSOLE_PORT=9001 All environment variables that these services need are set up in this file. This configuration file also contains information needed for these services to talk to each other. Notice that it is the use of environment variables that informs the MLFlow Tracking server how to access MinIO. In other words, the URL (including port number), the access key, the secret access key, and the bucket. This leads me to my final and most important point about using Docker Compose - the first time you bring up these services, the MLflow Tracking services will not work because you will need to first go into the MinIO UI, get your keys and create the bucket that the tracking service will use to store artifacts. Let’s do this now. Start the services for the first time using the command below. docker-compose --env-file config.env up -d --build Make sure you run this command in the same directory where your Docker Compose file is located. Now we can get our keys and create our bucket using the MinIO UI. The MinIO Console From your browser, go to `localhost:9001`. If you specified a different port for the MinIO console address in the `docker-compose` file, use that port instead. Sign in using the root user and password specified in the `config.env` file. Once you sign in, navigate to the Access Keys tab and click the Create access key button. This will take you to the Create Access Key page. Your access key and secret key are not saved until you click the Create button. Do not navigate away from this page until this is done. Don’t worry about copying the keys from this screen. Once you click the Create button, you will be given the option to download the keys to your file system (in a JSON file). If you want to use the MinIO SDK to manage raw data then create another access key and secret key while you are on this page. Next, create a bucket named `mlflow`. This is straightforward, go into the Buckets tab and click the `Create Bucket` button. Once you have your keys and you have created your bucket, you can finish setting up the services by stopping the containers, updating the `config.env`, and then restarting the containers. The command below will stop and remove your containers. docker-compose down To restart: docker-compose --env-file config.env up -d --build Let’s start the MLflow UI next and make sure everything is working. Starting the MLflow UI Navigate to `localhost:5000`. You should see the MLflow UI. Take some time to explore all the tabs. If you are new to MLflow, then get familiar with the concept of Runs and Experiments. A Run is a pass through your code that usually results in a trained model. Experiments are a way to tag related runs so that you can see them grouped together in the MLflow UI. For example, you may have trained several models using different parameters in an attempt to achieve the best accuracy (or performance). Tagging these runs with the same experiment name will group them together in the Experiments tab. Install the MLflow Python Package. The MLflow Python package is a simple `pip` install. I recommend installing it in a Python virtual environment. pip install mlflow Double check the installation by listing the MLflow library. pip list | grep mlflow You should see the library below. mlflow                      2.5.0 Install the MinIO Python Package You do not need to access MinIO directly to take advantage of MLflow features - the MLflow SDK will interface with the instance of MinIO we set up above. However, you may want to interface with this instance of MinIO directly to manage data before it is given to MLflow. MinIO is a great way to store all sorts of unstructured data. MinIO makes full use of underlying hardware, so you can save all the raw data you need without worrying about scale or . MinIO includes features to keep data in multiple locations synchronized. Also, someday AI will be regulated; when this day comes, you will need MinIO’s enterprise features ( , , , and ) to secure your data at rest and to make sure you do not accidentally delete something that a regulatory agency may request. performance bucket replication object locking versioning encryption legal locks If you installed the MLflow Python package in a virtual environment, install MinIO in the same environment. pip install minio Double check the installation. pip list | grep minio This will confirm that the Minio library was installed and display the version you are using. minio                    7.1.15 Summary This post provided an easy-to-follow recipe for setting up MLflow and MinIO on a development machine. The goal was to save you the time and effort of researching the MLflow servers and docker-compose configuration. Every effort was made to keep this recipe accurate. If you run into a problem, please let us know by dropping us a line at or joining the discussion on our . hello@min.io general Slack channel You are ready to start coding and training models with MLflow and MinIO. Also published . here

How to Set up a Development Machine with MLFlow and MinIO

কিভাবে MLFlow এবং MinIO এর সাথে একটি ডেভেলপমেন্ট মেশিন সেট আপ করবেন

So richten Sie eine Entwicklungsmaschine mit MLFlow und MinIO ein

Cómo configurar una máquina de desarrollo con MLFlow y MinIO

Comment configurer une machine de développement avec MLFlow et MinIO

एमएलफ्लो और मिनिओ के साथ एक विकास मशीन कैसे स्थापित करें

MLFlow と MinIO を使用して開発マシンをセットアップする方法

MLFlow 및 MinIO를 사용하여 개발 시스템을 설정하는 방법

Como configurar uma máquina de desenvolvimento com MLFlow e MinIO

Как настроить машину разработки с помощью MLFlow и MinIO

MLFlow ve MinIO ile Geliştirme Makinesi Nasıl Kurulur

Cách thiết lập máy phát triển với MLFlow và MinIO

如何使用 MLFlow 和 MinIO 设置开发机器

As the world of artificial intelligence (AI) and machine learning (ML) continues to grow, the demand for leveraging AI capabilities is slowly becoming overshadowed by the issues that organizations face with deploying and operationalizing AI capabilities. The combination of strong data science skillsets and ML development tools allows organizations to build optimal, efficient, and reproducible ML pipelines that produce state-of-the-art AI technologies. On the deployment side, there are tools and platforms that exist to support the operationalization and management of these technologies at scale. Unfortunately, these ML development and deployment tools often suffer compatibility issues and cannot interoperate, resulting in a fragmented ML lifecycle. What if there was a way to integrate these two stages of the ML lifecycle and decrease the average time from model development to deployment (which according to Gartner, takes on average, nine months) to only a few hours? Here at Modzy, we created an end-to-end integration that uses (a popular tool for ML training, tracking, and logging) to train an ML model and then automates the deployment process to the Modzy platform, thus creating an automated model deployment pipeline. MLflow Figure 1. Due to technological and organizational limitations, many companies support model development and model deployment as independent functions (A). This new MLflow-Modzy integration unifies these two stages to give Data Scientists full access to the complete model lifecycle (B) Modzy MLFlow Integration – Key Components MLflow Data scientists around the world leverage MLflow to enhance and streamline their experience during the ML model development process. As an example, consider a data scientist who wishes to log performance metrics and monitor them in real-time during training experiments. Specifically, this data scientist might want to train an image classification model and log the accuracy on a validation dataset while testing different combinations of hyperparameters. MLflow makes viewing and logging these metrics and parameters very easy with its user interface (UI), providing tables, graphs, and experiment details as training executes in real-time. Upon conclusion of this experiment, the data scientist will choose the set of hyperparameters that achieve the highest accuracy to tackle the challenge of moving this powerful model out of the lab and into production. Modzy Modzy makes the deployment and scaling of AI capabilities across an organization not only possible, but also streamlined and simple for . Once an ML model is trained, tested, and fine-tuned, Modzy provides a template to package these models in a Modzy-compatible Docker container. data scientists This process allows data scientists to save their model’s Docker image to a registry and upload the model to the Modzy platform, thereby unlocking endless potential for accessing and using AI capabilities through its powerful application programming interfaces (APIs) and software developer kits (SDKs). The Modzy team realized the significant value an end-to-end automated pipeline would bring to data scientists who already leverage MLflow or other common development tools (e.g., SageMaker, H20.ai, Neptune, WandB, Comet.ml) and produced an integration that: Trains a model, optimizes its hyperparameters, selects the best-performing model, and saves its model artifacts—all with full access to the native MLflow platform features (UI, logging, etc.) Sends these model artifacts to a model converter that plugs them into a ready-made repository (repo) and builds a docker image that conforms to the Modzy API specification, preparing the newly minted model to perform inference Deploys the model image and static metadata for the model to Modzy’s platform in order to make it available for use via Modzy’s marketplace Figure 2. Schematic showing the logical flow and components of the new MLflow integration, from a local developer to a machine learning model on the Modzy platform Model Trainer MLflow allows users to run as many training experiments as they wish—all while tracking the performance of different sets of hyperparameters. This Model Trainer portion of this integration gives the model developer the opportunity to test several hyperparameter sets during the training phase to find an optimal, best-performing model. Model Converter The output of a model training experiment is called a model artifact. Model artifacts can vary depending on the model, use case, or framework leveraged in training. Most commonly, model artifacts consist of model weights, labels, or any other configuration files required for the model to perform inference. In this integration, the model converter feeds the model artifacts (raw output of the Model Trainer) into a pre-built Github repo structured in a Modzy-compatible manner. Using this repo’s Dockerfile, the model converter then builds the Docker container image—which contains the model’s code and artifacts—and pushes this container image to a Docker registry. Model Importer After the model converter converts the model repository to a valid Docker container image, the model importer takes two inputs before uploading the model to Modzy’s platform: The container image Tar archive that contains model metadata The model metadata archive contains all marketplace information for the model, including technical model details, performance metrics, training information, images, and real-life usage examples. Value of Automated Model Deployment Ultimately, this integration takes a model from its experimentation phase and yields a production-ready model accessible via Modzy’s APIs and SDKs, expediting the time it takes to make this model available across an organization. The open architecture nature of Modzy’s platform makes this development-to-deployment automated pipeline adaptable to . Our solution opens the door to many new MLOps pipelines and decreases the amount of work required for data scientists and software developers to deploy models into production at an enterprise scale. other ML training tools

Using MLflow and Modzy for Automated Model Deployment

For organizations looking for a way to “democratize” data science, it is a must that data models are accessible to the enterprise in a very simple way. In our context, this is part of “model operationalization.” There are other solutions out there to serve data models which is a very common problem for data scientists. We’ve thought of having a REST API to handle running data models on a HTTP post with new data. Then, came — which allows serving data models as REST API without the complicated setup. MLFlow To serve models using MLFlow, we did the following: 1. Save R models as RDS format using the saveRDS function. 2. Convert RDS format into MLFLOW flavor using the mlflow_save_model(). model = readRDS(“./LR_Model.rds”)
predictor <- crate(~ stats::predict.lm(model, as.data.frame(.x)), model)
mlflow_save_model(predictor, “< path_to_save >”) 3. Commit to Git. 4. Setup a server to host the model. Clone git repo. 5. Serve model using the mlflow_rfunc_serve(). mlflow_rfunc_serve( , run_uuid = , host = ,port = ) "< path_to_save >" NULL "127.0.0.1" 8090 6. On users’ jupyter notebook, we have the following R lines to query the rest api endpoint with the variable storing the model response. output request_body_json <- toJSON(feature_dataframe[column_name_features],dataframe= )
request_body_json
result = POST(url = paste0( , ),body=request_body_json,add_headers(.headers = c( = , = )))
output <- content(result) 'rows' 'http://127.0.0.1:8090/' 'predict/' "Content-Type" "application/json" "Accept" "application/json" 7. On python, a similar function is available import requests
import json
def mlflow_predict(dd,model_name= ):
    url = headers = { : , : }
    resp = requests.post(url=url,json=dd,headers=headers)
    res=json.loads(resp.content.decode( )) res[ ] 'LR12' "http://127.0.0.1:8090/predict/" "Accept" "application/json" "Content-Type" "application/json" "utf-8" return 'predictions' If you’re deploying on AWS, make sure to set host=”0.0.0.0" to make it listen to the public internet address. For Python-based models, MLFLow supports deploying to SageMaker. If you have multiple models to serve, MLFLOW assigns a port to each of them. Make sure you have a working R and Python installations. We used anaconda3 to setup the environment. Also, at least 1GB of RAM is needed to get R running with MLFlow in AWS LightSail.

Serve Data Models with MLFlow in Production

Dear R Systems Community, Dear R Systems Community, We’re excited to announce that Round 2 of the R Systems Blogbook—Chapter 1 is officially open for submissions! 🎉 Round 2 R Systems Blogbook—Chapter 1 This writing contest, sponsored by R Systems, offers R Systems employees a unique opportunity to share their expertise, real-world experiences, technical projects, and case studies with HackerNoon’s global community. R Systems So, if you’re part of the R Systems family and looking to showcase how your skills and experiences contribute to R Systems' leadership in digital transformation, establish yourself as a thought leader, and win some awesome prizes along the way, this contest is tailor-made for you! Round 1 Recap The first round of the R Systems BlogBook—Chapter 1 launched in February this year, challenging R Systems staff to share insights on two topics: AI/ML Model Deployment with MLflow and Kubernetes
Enhancing Developer Productivity with GitHub Copilot and Other AI Tools AI/ML Model Deployment with MLflow and Kubernetes AI/ML Model Deployment with MLflow and Kubernetes Enhancing Developer Productivity with GitHub Copilot and Other AI Tools Enhancing Developer Productivity with GitHub Copilot and Other AI Tools When submissions for the round closed, HackerNoon’s editors had the difficult job of combing through inspired submissions to select winning entries as follows: Runner up: Shashi Prakash Patel for AI/ML Model Deployment with MLflow & Kubernetes: From Experimentation to Enterprise-Grade Deployment
Runner up: Vimaldeep Singh for How My Development Team Utilized GitHub Copilot & AI Tools to Boost Productivity
Runner up: Aditya Mishra for Developer Productivity with GitHub Copilot & AI Tools
Grand Prize winner: Preeti Verma for How GitHub Copilot Enhances Developer Productivity Runner up: Shashi Prakash Patel for AI/ML Model Deployment with MLflow & Kubernetes: From Experimentation to Enterprise-Grade Deployment AI/ML Model Deployment with MLflow & Kubernetes: From Experimentation to Enterprise-Grade Deployment AI/ML Model Deployment with MLflow & Kubernetes: From Experimentation to Enterprise-Grade Deployment Runner up: Vimaldeep Singh for How My Development Team Utilized GitHub Copilot & AI Tools to Boost Productivity How My Development Team Utilized GitHub Copilot & AI Tools to Boost Productivity How My Development Team Utilized GitHub Copilot & AI Tools to Boost Productivity Runner up: Aditya Mishra for Developer Productivity with GitHub Copilot & AI Tools Developer Productivity with GitHub Copilot & AI Tools Developer Productivity with GitHub Copilot & AI Tools Grand Prize winner: Preeti Verma for How GitHub Copilot Enhances Developer Productivity How GitHub Copilot Enhances Developer Productivity How GitHub Copilot Enhances Developer Productivity Big congratulations once again to all of Round 1’s winners! Big congratulations once again to all of Round 1’s winners! If you missed out last time — or are ready for another shot — now’s your chance to shine. If you missed out last time — or are ready for another shot — now’s your chance to shine. If you missed out last time — or are ready for another shot — now’s your chance to shine. Round 2 Details Submissions window: April 29 — May 30, 2025 April 29 — May 30, 2025 Contest topics: Contest topics: Microservices Observability: Leveraging OpenTelemetry
Implementing Zero Trust Security in Cloud-Native Environments Microservices Observability: Leveraging OpenTelemetry Microservices Observability: Leveraging OpenTelemetry Implementing Zero Trust Security in Cloud-Native Environments Implementing Zero Trust Security in Cloud-Native Environments How to Participate

Pick one of the contest topics above.
Write an article that's at least 800 words long (shorter submissions will be automatically rejected).
Submit your article via this form: [ R Systems BlogBook—Chapter 1: Round 2 Submission Form ] How to Participate Pick one of the contest topics above.
Write an article that's at least 800 words long (shorter submissions will be automatically rejected).
Submit your article via this form: [ R Systems BlogBook—Chapter 1: Round 2 Submission Form ] Pick one of the contest topics above. Write an article that's at least 800 words long (shorter submissions will be automatically rejected). at least 800 words Submit your article via this form: [ R Systems BlogBook—Chapter 1: Round 2 Submission Form ] R Systems BlogBook—Chapter 1: Round 2 Submission Form Prizes Grand Prize (for best entry): HackerNoon<> R Systems Laptop Sleeve + Blogging Badge Grand Prize (for best entry): HackerNoon<> R Systems Laptop Sleeve + Blogging Badge Grand Prize (for best entry): HackerNoon<> R Systems Laptop Sleeve + Blogging Badge 3 Runners-up prizes: HackerNoon<> R Systems Blogging Badges 3 Runners-up prizes: HackerNoon<> R Systems Blogging Badges 3 Runners-up prizes: HackerNoon<> R Systems Blogging Badges We can’t wait to see what you come up with—Good luck! We can’t wait to see what you come up with—Good luck! Want to stay in the loop on other HackerNoon contests? Subscribe here or visit contests.hackernoon.com Want to stay in the loop on other HackerNoon contests? Subscribe here or visit contests.hackernoon.com Subscribe here Subscribe here Subscribe here contests.hackernoon.com contests.hackernoon.com contests.hackernoon.com

R Systems BlogBook—Chapter 1: Round 2 is Now Open for Submissions🎉

Remember when prompt engineering meant clever ChatGPT hacks and intuition-driven guesswork? Those days are long gone. As large language models (LLMs) become embedded in enterprise workflows, the tools we use to build with them need to grow up too. Today, prompt engineering is shifting from creativity and trial-and-error to something that resembles software development. It’s about building systems that are testable, observable, and improvable. Whether you’re designing agents for production or experimenting with multi-step pipelines, you need tools that let you optimize prompts systematically. This article explores eight projects that are redefining prompt engineering. From visual workflows to auto-tuned prompts, these tools help you scale your LLM projects without losing control or clarity. 1. AdalFlow – Build and Auto-Optimize LLM Applications AdalFlow AdalFlow is a PyTorch-inspired framework that lets developers build and optimize LLM workflows declaratively. Its core strength is combining expressive Python APIs with automatic optimization for latency, performance, and cost. Key Concepts: Key Concepts: FlowModule: Just like a PyTorch nn.Module, you can define your own reusable building blocks for LLM workflows, including routing logic, RAG components, or agents.
AutoDiff + Static Graph Compilation: Behind the scenes, AdalFlow compiles your FlowModule into an efficient DAG, minimizing unnecessary LLM calls.
Decoupled Execution: You define logic once and can then execute it locally, remotely, or in streaming mode using pluggable executors. FlowModule: Just like a PyTorch nn.Module, you can define your own reusable building blocks for LLM workflows, including routing logic, RAG components, or agents. FlowModule: nn.Module AutoDiff + Static Graph Compilation: Behind the scenes, AdalFlow compiles your FlowModule into an efficient DAG, minimizing unnecessary LLM calls. AutoDiff + Static Graph Compilation: FlowModule Decoupled Execution: You define logic once and can then execute it locally, remotely, or in streaming mode using pluggable executors. Decoupled Execution: Example Use Case: You can construct an AgentFlowModule that combines retrieval (via RAG), structured prompt formatting, and function call-style output validation—all in one unified pipeline. Example Use Case: AgentFlowModule AdalFlow is designed for production-grade LLM apps with strict latency budgets and clear reliability requirements. 2. Ape – Your First AI Prompt Engineer Ape Ape, created by Weavel, is a prompt engineer co-pilot that helps you test, debug, and improve your LLM applications. It is designed to eliminate the need for gut-based prompt tuning by giving developers structured, inspectable feedback on how their agents behave. What It Does: What It Does: Capture and Replay Traces: Ape records every prompt, tool call, response, and retry in a session. You can replay specific steps or re-run chains with modified prompts to see how behavior changes.
Prompt Iteration Comparison: It supports side-by-side comparisons between different prompt versions, allowing you to benchmark performance, accuracy, or hallucination reduction. Capture and Replay Traces: Ape records every prompt, tool call, response, and retry in a session. You can replay specific steps or re-run chains with modified prompts to see how behavior changes. Capture and Replay Traces: Prompt Iteration Comparison: It supports side-by-side comparisons between different prompt versions, allowing you to benchmark performance, accuracy, or hallucination reduction. Prompt Iteration Comparison: Why It’s Powerful: Ape acts like your first prompt engineer hire—automating the trial-and-error loop with traceability and insight. Instead of asking “what went wrong?” you get to see exactly how the agent behaved and what led to it. Why It’s Powerful: 3. AutoRAG – Evaluate and Optimize RAG Pipelines Automatically AutoRAG AutoRAG is an open-source framework that helps you build, evaluate, and optimize Retrieval-Augmented Generation (RAG) pipelines using your own data. It’s ideal for developers and researchers who want to test different RAG setups—like chunking strategies, retrievers, and rankers—without rebuilding the whole pipeline manually. Core Features: Core Features: Plug-and-Play Modules: Includes modular implementations of common RAG components: embedding models (e.g. OpenAI, Cohere), chunkers, retrievers (e.g. FAISS), rankers, and response generators.
RAG Benchmarking: Define an evaluation set (context + query + expected answer), and AutoRAG will automatically compare different pipelines using metrics like EM (Exact Match), F1, ROUGE, and BLEU.
Pipeline Search: Automatically evaluates combinations of modules and hyperparameters to find the best-performing configuration on your data.
Dashboard: Provides a clean web-based UI to visualize pipeline performance, outputs, and comparison metrics. Plug-and-Play Modules: Includes modular implementations of common RAG components: embedding models (e.g. OpenAI, Cohere), chunkers, retrievers (e.g. FAISS), rankers, and response generators. Plug-and-Play Modules: RAG Benchmarking: Define an evaluation set (context + query + expected answer), and AutoRAG will automatically compare different pipelines using metrics like EM (Exact Match), F1, ROUGE, and BLEU. RAG Benchmarking: Pipeline Search: Automatically evaluates combinations of modules and hyperparameters to find the best-performing configuration on your data. Pipeline Search: Dashboard: Provides a clean web-based UI to visualize pipeline performance, outputs, and comparison metrics. Dashboard: Why It Matters: Designing a RAG pipeline involves many moving parts: how you chunk documents, which embedding model you use, what retriever to apply, etc. AutoRAG automates this experimentation process, saving hours of trial-and-error and helping you find optimal setups fast. Why It Matters: 4. DSPy – The framework for programming, not prompting language models DSPy DSPy is a powerful framework from Stanford NLP that brings structure and optimization to prompt engineering by treating LLM components like programmable modules. Core Abstraction: Core Abstraction: Signatures: You define a Signature (input/output schema) for each module—for example, a summarizer takes in a paragraph and returns a concise sentence.
Modules: Instead of writing prompts manually, you compose your app from building blocks like:

Predict – simple generation
Select – ranking or classification tasks
ChainOfThought – multi-step reasoning
RAG – retrieval-augmented modules


Optimizers: DSPy comes with built-in optimizers like COPRO, which run experiments to find the best prompt structure, formatting, and LLM configuration using few-shot or retrieval-based techniques. Signatures: You define a Signature (input/output schema) for each module—for example, a summarizer takes in a paragraph and returns a concise sentence. Signatures: Signature Modules: Instead of writing prompts manually, you compose your app from building blocks like:

Predict – simple generation
Select – ranking or classification tasks
ChainOfThought – multi-step reasoning
RAG – retrieval-augmented modules Modules: Predict – simple generation
Select – ranking or classification tasks
ChainOfThought – multi-step reasoning
RAG – retrieval-augmented modules Predict – simple generation Predict Select – ranking or classification tasks Select ChainOfThought – multi-step reasoning ChainOfThought RAG – retrieval-augmented modules RAG Optimizers: DSPy comes with built-in optimizers like COPRO, which run experiments to find the best prompt structure, formatting, and LLM configuration using few-shot or retrieval-based techniques. Optimizers: COPRO Key Features: Key Features: Reproducible Pipelines: You can define LLM workflows as reusable Python classes with structured inputs/outputs.
Auto-Tuning: Run evaluations on labeled datasets and let DSPy optimize prompt phrasing or example selection automatically.
MLFlow Integration: Track experiments, prompt variants, and performance metrics over time. Reproducible Pipelines: You can define LLM workflows as reusable Python classes with structured inputs/outputs. Reproducible Pipelines: Auto-Tuning: Run evaluations on labeled datasets and let DSPy optimize prompt phrasing or example selection automatically. Auto-Tuning: MLFlow Integration: Track experiments, prompt variants, and performance metrics over time. MLFlow Integration: Why It Matters: DSPy brings ML-style engineering workflows to LLM development. It’s not just a wrapper—it’s an ecosystem for building, testing, and optimizing modular LLM applications. Why It Matters: 5. Zenbase – Programming, Not Prompting, for AI in Production Zenbase Zenbase Core is the library for programming—not prompting—AI in production. It is a spin-out of Stanford NLP's DSPy project and is led by several of its key contributors. While DSPy is excellent for research and experimentation, Zenbase focuses on turning those ideas into tools suitable for production environments. It brings the power of structured memory, retrieval, and LLM orchestration into the software engineering workflow. Key Points: Key Points: DSPy vs Zenbase: DSPy is built for R&D, where developers test and evaluate ideas. Zenbase adapts those ideas for production, emphasizing reliability, maintainability, and deployment-readiness.


Automatic Prompt Optimization: Zenbase enables automatic optimization of prompts and retrieval logic in real-world applications, integrating seamlessly into existing pipelines.


Engineering Focus: Designed for software teams that need composable, debuggable LLM programs that evolve beyond prototype. DSPy vs Zenbase: DSPy is built for R&D, where developers test and evaluate ideas. Zenbase adapts those ideas for production, emphasizing reliability, maintainability, and deployment-readiness. DSPy vs Zenbase: DSPy is built for R&D, where developers test and evaluate ideas. Zenbase adapts those ideas for production, emphasizing reliability, maintainability, and deployment-readiness. DSPy vs Zenbase: Automatic Prompt Optimization: Zenbase enables automatic optimization of prompts and retrieval logic in real-world applications, integrating seamlessly into existing pipelines. Automatic Prompt Optimization: Zenbase enables automatic optimization of prompts and retrieval logic in real-world applications, integrating seamlessly into existing pipelines. Automatic Prompt Optimization: Engineering Focus: Designed for software teams that need composable, debuggable LLM programs that evolve beyond prototype. Engineering Focus: Designed for software teams that need composable, debuggable LLM programs that evolve beyond prototype. Engineering Focus: Zenbase is ideal for developers who want to treat prompt engineering as real engineering—modular, testable, and built for scale. 6. AutoPrompt – Prompt tuning using Intent-based Prompt Calibration AutoPrompt AutoPrompt is a lightweight framework for automatically improving prompt performance based on real data and model feedback. Rather than relying on manual iterations or human intuition, AutoPrompt uses an optimization loop to refine prompts for your specific task and dataset. Why It Matters: Prompt tuning typically involves testing dozens of phrasing variations by hand. AutoPrompt automates this, discovers blind spots, and continuously improves the prompt—turning prompt writing into a measurable and scalable process. Why It Matters: 7. EvoPrompt – Evolutionary Algorithms for Prompt Search EvoPrompt EvoPrompt is a Microsoft-backed research project that applies evolutionary algorithms to optimize prompts. It reframes prompt crafting as a population-based search problem: generate many prompts, evaluate their fitness, and evolve the best-performing ones through mutation and crossover. How It Works: How It Works: Initial Population: Start with a set of candidate prompts for a specific task.
Evaluation: Each prompt is scored using a defined metric (e.g., accuracy, BLEU, human eval).
Genetic Evolution:

Mutation introduces small, random changes to improve performance.
Crossover combines high-performing prompts into new variants.
Selection keeps the top-performing prompts for the next generation.


Iteration: The process repeats over multiple generations until performance converges. Initial Population: Start with a set of candidate prompts for a specific task. Initial Population: Evaluation: Each prompt is scored using a defined metric (e.g., accuracy, BLEU, human eval). Evaluation: Genetic Evolution:

Mutation introduces small, random changes to improve performance.
Crossover combines high-performing prompts into new variants.
Selection keeps the top-performing prompts for the next generation. Genetic Evolution: Mutation introduces small, random changes to improve performance.
Crossover combines high-performing prompts into new variants.
Selection keeps the top-performing prompts for the next generation. Mutation introduces small, random changes to improve performance. Mutation Crossover combines high-performing prompts into new variants. Crossover Selection keeps the top-performing prompts for the next generation. Selection Iteration: The process repeats over multiple generations until performance converges. Iteration: Supported Algorithms: Supported Algorithms: Genetic Algorithm (GA)
Differential Evolution (DE)
Tree-based crossover operations using LLMs Genetic Algorithm (GA) Differential Evolution (DE) Tree-based crossover operations using LLMs Why It Matters: Writing the perfect prompt is hard—even harder when doing it at scale. EvoPrompt turns prompt design into a computational optimization problem, giving you measurable gains without human micromanagement. Why It Matters: 8. Promptimizer – Feedback-Driven Prompt Evaluation and Optimization Promptimizer Promptimizer is an experimental Python library for optimizing prompts using feedback loops from LLMs or human raters. Unlike frameworks that focus purely on generation or evaluation, Promptimizer creates a structured pipeline for systematically improving prompt quality over time. Why It Matters: Promptimizer gives prompt engineering the same kind of feedback loop you’d expect in UX testing or ML training: test, measure, improve. It’s especially powerful for copywriting, content generation, and any task where subjective quality matters. Why It Matters: Why These Tools Matter These tools are transforming prompt engineering from an art into a disciplined engineering practice: Cost Control: Optimized prompts use fewer tokens, directly reducing API expenditures.
Speed: Tools like AdalFlow and AutoRAG decrease development time from days to minutes.
Accuracy: Frameworks like EvoPrompt enhances benchmark scores by up to 15%.
Governance: Systems such as Ape and DSPy support auditability and repeatability. Cost Control: Optimized prompts use fewer tokens, directly reducing API expenditures. Cost Control: Speed: Tools like AdalFlow and AutoRAG decrease development time from days to minutes. Speed: Accuracy: Frameworks like EvoPrompt enhances benchmark scores by up to 15%. Accuracy: Governance: Systems such as Ape and DSPy support auditability and repeatability. Governance: Prompt engineering is no longer just a skill—it has evolved into a comprehensive stack. Final Thoughts The future of LLM applications doesn’t belong to clever hacks but to scalable infrastructure. Whether you're addressing workflow complexity with AdalFlow, debugging agents with Ape, or optimizing instructions with AutoPrompt and EvoPrompt, these tools elevate you from intuition-based methods to reliable engineering practices. The return on investment is tangible: from sub-$1 optimization runs to significant conversion boosts, effective prompting proves its value. Looking ahead, we anticipate tighter integrations with fine-tuning, multi-modal prompt design, and prompt security scanners. The message is clear: The era of artisanal prompting is behind us. Welcome to industrial-grade prompt engineering. Build better prompts. Build better systems. The era of artisanal prompting is behind us. Welcome to industrial-grade prompt engineering. Build better prompts. Build better systems.

Escape Prompt Hell With These 8 Must-have Open-source Tools

Escape Prompt Hell مع هذه 8 أدوات مفتوحة المصدر

Избягайте от ада с тези 8 инструмента с отворен код

Escape Prompt Hell con estas 8 herramientas de código abierto imprescindibles

Escape Prompt Hell With These 8 Must-Have オープンソースツール

Escape Prompt Hell ამ 8 Must-Have Open-Source ინსტრუმენტები

ការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយការបាត់បន្ថយ

Escape Prompt Hell med dessa 8 måste-ha öppen källkod verktyg

Escape Prompt Hell gamit ang mga 8 Must-Have Open-Source Tools

Bu 8 must-have open-source faylalarla Escape Prompt Hell

Greetings, R Systems Team! The wait is over—we’re thrilled to announce that the first round of R Systems BlogBook—Chapter 1 is officially live! 🎉 This writing contest is a unique collaboration between R Systems and HackerNoon designed exclusively for you—R Systems staff. It is your chance to showcase how your skills and experience contribute to R Systems’ leadership in digital transformation, establish yourself as a thought leader within HackerNoon's global community of technologists, and win some more fantastic prizes along the way! Contest Structure The contest is divided into 3 rounds, each lasting a month: Round 1: Feb 25 – Mar 24


Round 2: Mar 25 – Apr 22


Round 3: Apr 23 – May 21 Each Round, You’ll be given two topics to choose from, as follows: Round 1 Topics [Submissions Window: Feb 25 - Mar 24] AI/ML Model Deployment with MLflow and Kubernetes Share how R Systems tackles AI/ML model deployment MLflow and Kubernetes, using a project you’ve worked on/contributed to as a case study. Enhancing Developer Productivity with GitHub Copilot and Other AI Tools Based on your experience, demonstrate how tools like GitHub Copilot are used to enhance developer productivity. What’s Coming in Later Rounds? Round 2 Topics [Submissions Window: Mar 25 – Apr 22] Microservices Observability: Leveraging OpenTelemetry


Implementing Zero Trust Security in Cloud-Native Environments. Round 3 Topics [Submissions Window: Apr 23 – May 21] Explore the Capabilities of WebAssembly for High-Performance Web Applications.
Integrating Generative AI into SaaS Applications: Best Practices and Use Cases. How to Participate in R Systems BlogBook—Chapter 1 Pick a topic to write on from the round’s list.
Write your article in a Google Doc and make sure it’s shareable.
Make sure your article is at least 800 words long—anything shorter will be automatically rejected.
Submit it using this form: [R Systems BlogBook—Chapter 1: Submissions Form] Prizes The contest will award up to 12 winners (4 winners per round) Up for grabs each month: Grand Prize (for best entry): HackerNoon<> R Systems Laptop Sleeve + Blogging Badge 3 Runners-up prizes: HackerNoon<> R Systems Blogging Badges We can’t wait to see what you come up with—Good luck! Want to stay in the loop on other HackerNoon contests? Subscribe here or visit contests.hackernoon.com Greetings, R Systems Team! The wait is over—we’re thrilled to announce that the first round of R Systems BlogBook—Chapter 1 is officially live! 🎉 R Systems BlogBook—Chapter 1 This writing contest is a unique collaboration between R Systems and HackerNoon designed exclusively for you—R Systems staff. It is your chance to showcase how your skills and experience contribute to R Systems’ leadership in digital transformation, establish yourself as a thought leader within HackerNoon's global community of technologists, and win some more fantastic prizes along the way! exclusively for you—R Systems staff. Contest Structure The contest is divided into 3 rounds, each lasting a month: The contest is divided into 3 rounds, each lasting a month: Round 1: Feb 25 – Mar 24 Round 2: Mar 25 – Apr 22 Round 3: Apr 23 – May 21 Round 1: Feb 25 – Mar 24 Round 1: Feb 25 – Mar 24 Round 1: Round 2: Mar 25 – Apr 22 Round 2: Mar 25 – Apr 22 Round 2: Round 3: Apr 23 – May 21 Round 3: Apr 23 – May 21 Round 3: Each Round, You’ll be given two topics to choose from, as follows: Each Round, You’ll be given two topics to choose from, as follows: Round 1 Topics Round 1 Topics [Submissions Window: Feb 25 - Mar 24 ] Feb 25 - Mar 24 AI/ML Model Deployment with MLflow and Kubernetes AI/ML Model Deployment with MLflow and Kubernetes AI/ML Model Deployment with MLflow and Kubernetes Share how R Systems tackles AI/ML model deployment MLflow and Kubernetes, using a project you’ve worked on/contributed to as a case study. Share how R Systems tackles AI/ML model deployment MLflow and Kubernetes, using a project you’ve worked on/contributed to as a case study. Enhancing Developer Productivity with GitHub Copilot and Other AI Tools Enhancing Developer Productivity with GitHub Copilot and Other AI Tools Enhancing Developer Productivity with GitHub Copilot and Other AI Tools Based on your experience, demonstrate how tools like GitHub Copilot are used to enhance developer productivity. Based on your experience, demonstrate how tools like GitHub Copilot are used to enhance developer productivity. What’s Coming in Later Rounds? What’s Coming in Later Rounds? Round 2 Topics Round 2 Topics [Submissions Window: Mar 25 – Apr 22 ] Mar 25 – Apr 22 Microservices Observability: Leveraging OpenTelemetry Implementing Zero Trust Security in Cloud-Native Environments. Microservices Observability: Leveraging OpenTelemetry Microservices Observability: Leveraging OpenTelemetry Implementing Zero Trust Security in Cloud-Native Environments. Implementing Zero Trust Security in Cloud-Native Environments. Round 3 Topics Round 3 Topics [Submissions Window: Apr 23 – May 21 ] Apr 23 – May 21 Explore the Capabilities of WebAssembly for High-Performance Web Applications. Integrating Generative AI into SaaS Applications: Best Practices and Use Cases. Explore the Capabilities of WebAssembly for High-Performance Web Applications. Integrating Generative AI into SaaS Applications: Best Practices and Use Cases. How to Participate in R Systems BlogBook—Chapter 1 Pick a topic to write on from the round’s list.
Write your article in a Google Doc and make sure it’s shareable.
Make sure your article is at least 800 words long—anything shorter will be automatically rejected.
Submit it using this form: [R Systems BlogBook—Chapter 1: Submissions Form] Pick a topic to write on from the round’s list. Write your article in a Google Doc and make sure it’s shareable. Make sure your article is at least 800 words long—anything shorter will be automatically rejected. Submit it using this form: [R Systems BlogBook—Chapter 1: Submissions Form] Pick a topic to write on from the round’s list. Write your article in a Google Doc and make sure it’s shareable. Make sure your article is at least 800 words long— anything shorter will be automatically rejected. 800 words anything shorter will be automatically rejected. Submit it using this form: [ R Systems BlogBook—Chapter 1: Submissions Form ] R Systems BlogBook—Chapter 1: Submissions Form R Systems BlogBook—Chapter 1: Submissions Form Prizes The contest will award up to 12 winners (4 winners per round) The contest will award up to 12 winners (4 winners per round) Up for grabs each month: Up for grabs each month: Grand Prize (for best entry): HackerNoon<> R Systems Laptop Sleeve + Blogging Badge Grand Prize (for best entry): HackerNoon<> R Systems Laptop Sleeve + Blogging Badge Grand Prize (for best entry): HackerNoon<> R Systems Laptop Sleeve + Blogging Badge 3 Runners-up prizes: HackerNoon<> R Systems Blogging Badges 3 Runners-up prizes: HackerNoon<> R Systems Blogging Badges 3 Runners-up prizes: HackerNoon<> R Systems Blogging Badges We can’t wait to see what you come up with—Good luck! We can’t wait to see what you come up with—Good luck! Want to stay in the loop on other HackerNoon contests? Subscribe here or visit contests.hackernoon.com Want to stay in the loop on other HackerNoon contests? Subscribe here or visit contests.hackernoon.com Subscribe here Subscribe here contests.hackernoon.com contests.hackernoon.com

R Systems Blogbook—Chapter 1 is Now Open for Submissions🎉

Seasons greetings, folks! 🌲🥂🎉 ’Tis the season to be merry, and in that spirit, we’re thrilled to announce a unique partnership between R Systems and HackerNoon for a first-of-its-kind internal writing contest: R Systems BlogBook — Chapter 1. This contest is designed exclusively for R Systems staff to share your experiences, projects, and case studies, showcasing the hard work you've put in and how it contributes to R Systems' leadership in the digital transformation industry and its unique approach to software product engineering. Key Contest Info Duration: 3 months,
Rounds: 3 (one per month)
Total Winners: 12 (4 winners per round)
Prizes:

Grand Prize: Co-branded Laptop Sleeve
Top Participants: Co-branded Blogger Badge Contest Structure The contest kicks off in January 2025 (exact date TBA).
Each round will focus on two writing prompts, with one month for submissions.
After each round, the editorial team will review ALL eligible submissions and select the top four stories. These will be featured on R Systems' blog and subsequently showcased on the company’s business page on HackerNoon. Participants are to use their projects and experience at R Systems to tackle one of two writing prompts each round, as follows: Writing Prompts by Round Round 1 AI/ML Model Deployment with MLflow and Kubernetes
Enhancing Developer Productivity with GitHub Copilot and Other AI Tools Round 2 Microservices Observability: Leveraging OpenTelemetry
Implementing Zero Trust Security in Cloud-Native Environments Round 3 Exploring the Capabilities of WebAssembly for High-Performance Web Applications


Integrating Generative AI into SaaS Applications: Best Practices and Use Cases Interested participants are encouraged to start brainstorming ideas and stay tuned for the official announcement with full details. Be the first to know when we share new updates by subscribing to the HackerNoon Contests Announcement Page [here]. We can’t wait to read your work! Seasons greetings, folks! 🌲🥂🎉 ’Tis the season to be merry, and in that spirit, we’re thrilled to announce a unique partnership between R Systems and HackerNoon for a first-of-its-kind internal writing contest: R Systems BlogBook — Chapter 1. R Systems BlogBook — Chapter 1. R Systems BlogBook — Chapter 1. This contest is designed exclusively for R Systems staff to share your experiences, projects, and case studies, showcasing the hard work you've put in and how it contributes to R Systems' leadership in the digital transformation industry and its unique approach to software product engineering. Key Contest Info Key Contest Info Duration: 3 months, Rounds: 3 (one per month) Total Winners: 12 (4 winners per round) Prizes:

Grand Prize: Co-branded Laptop Sleeve
Top Participants: Co-branded Blogger Badge Duration: 3 months, Duration: Rounds: 3 (one per month) Rounds: Total Winners: 12 (4 winners per round) Total Winners: Prizes: Grand Prize: Co-branded Laptop Sleeve
Top Participants: Co-branded Blogger Badge Prizes: Grand Prize: Co-branded Laptop Sleeve Top Participants: Co-branded Blogger Badge Grand Prize: Co-branded Laptop Sleeve Grand Prize: Top Participants: Co-branded Blogger Badge Top Participants: Contest Structure Contest Structure The contest kicks off in January 2025 (exact date TBA). Each round will focus on two writing prompts, with one month for submissions. After each round, the editorial team will review ALL eligible submissions and select the top four stories. These will be featured on R Systems' blog and subsequently showcased on the company’s business page on HackerNoon. The contest kicks off in January 2025 (exact date TBA). January 2025 (exact date TBA). Each round will focus on two writing prompts, with one month for submissions. After each round, the editorial team will review ALL eligible submissions and select the top four stories. These will be featured on R Systems' blog and subsequently showcased on the company’s business page on HackerNoon. Participants are to use their projects and experience at R Systems to tackle one of two writing prompts each round, as follows: Participants are to use their projects and experience at R Systems to tackle one of two writing prompts each round, as follows: Writing Prompts by Round Round 1 Round 1 AI/ML Model Deployment with MLflow and Kubernetes Enhancing Developer Productivity with GitHub Copilot and Other AI Tools AI/ML Model Deployment with MLflow and Kubernetes Enhancing Developer Productivity with GitHub Copilot and Other AI Tools Round 2 Round 2 Microservices Observability: Leveraging OpenTelemetry Implementing Zero Trust Security in Cloud-Native Environments Microservices Observability: Leveraging OpenTelemetry Implementing Zero Trust Security in Cloud-Native Environments Round 3 Round 3 Exploring the Capabilities of WebAssembly for High-Performance Web Applications Integrating Generative AI into SaaS Applications: Best Practices and Use Cases Exploring the Capabilities of WebAssembly for High-Performance Web Applications Exploring the Capabilities of WebAssembly for High-Performance Web Applications Integrating Generative AI into SaaS Applications: Best Practices and Use Cases Integrating Generative AI into SaaS Applications: Best Practices and Use Cases Interested participants are encouraged to start brainstorming ideas and stay tuned for the official announcement with full details. Be the first to know when we share new updates by subscribing to the HackerNoon Contests Announcement Page [here]. Be the first to know when we share new updates by subscribing to the HackerNoon Contests Announcement Page [ here ]. here here We can’t wait to read your work!

Coming Soon: R Systems BlogBook – Chapter 1, Powered by HackerNoon

Let's Build an MLOps Pipeline With Databricks and Spark - Part 1

An Architect's Guide to Machine Learning Operations and Required Data Infrastructure

Mä Arquitecto ukan Guía de Operaciones de Aprendizaje Máquina ukat Infraestructura de Datos Requeridos ukanakataki

মেশিন লার্নিং অপারেশন এবং প্রয়োজনীয় ডেটা অবকাঠামোর জন্য একজন স্থপতির গাইড

Guía para arquitectos sobre operaciones de aprendizaje automático e infraestructura de datos necesaria

Vodič za arhitekte za operacije strojnog učenja i potrebnu podatkovnu infrastrukturu

Una guida per architetti alle operazioni di apprendimento automatico e all'infrastruttura dati richiesta

機械学習の運用と必要なデータインフラストラクチャに関するアーキテクト向けガイド

არქიტექტორის გზამკვლევი მანქანათმცოდნეობის ოპერაციებისა და საჭირო მონაცემთა ინფრასტრუქტურისთვის

Машиналық оқыту операциялары және қажетті деректер инфрақұрылымы бойынша сәулетші нұсқаулығы

Mašininio mokymosi operacijų ir būtinos duomenų infrastruktūros architekto vadovas

Torolàlana ho an'ny Architect ho an'ny fiasan'ny fianarana milina sy fotodrafitrasa angon-drakitra ilaina

Panduan Arkitek untuk Operasi Pembelajaran Mesin dan Infrastruktur Data yang Diperlukan

Një udhëzues arkitekti për operacionet e mësimit të makinerisë dhe infrastrukturën e kërkuar të të dhënave

Stories, News & Mentions about MLOFLO

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