The Best ML Notebooks And Infrastructure Tools For Data Scientists

The Best ML Notebooks And Infrastructure Tools For Data Scientists

Machine learning or data science notebooks have become an integral tool for data scientists across the world. Notebooks are highly-interactive multi-purpose tools that not only let you write and execute code but, at the same time, analyse intermediate results to gain insights (using tables or visualisations) while working on a project.

Below is our list of the best data science notebooks in the business, based on four main parameters: language support, version control, data visualisation capabilities, and cost-efficiency.

Jupyter Notebooks

Jupyter Notebook is an open-source platform that supports more than 40 programming languages, including R and Python. ipynb, the default format for Jupyter files, is a JSON file and can be easily version controlled and shared using email, Dropbox, Github, and Jupyter Notebook Viewer. Jupyter Notebook supports big data integration through Apache Spark, a top analytics engine for in-memory data processing. The platform also offers popular libraries such as matplotlib, pandas, scikit-learn, ggplot2, and TensorFlow to enable seamless integration of data analytics, machine learning code, and data visualisations while working on the project.

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Kaggle Notebooks

Kaggle, an online community of data scientists, hosts Jupyter notebooks for R and Python. Kaggle Notebooks can be created and edited via a notebook editor with an editing window, a console, and a setting window. Kaggle hosts a vast number of publicly available datasets. Besides, you can also output files from a different Notebook or upload your own dataset. Kaggle comes with a powerful collaboration feature that lets multiple users co-own and edit a Notebook. It also offers a robust computational environment to add GPUs or TPUs. Kaggle gives 9 hours of execution time.

Google Colab 

Collaboratory, the Google platform for hosting Jupyter notebooks, allows you to write Python in your browser with no configuration, free access to GPUs, and easy sharing. All Python libraries and machine learning frameworks are available, and the notebook code is executed on Google cloud. You can load data from Github, Google Drive, or a local drive. The machine learning community extensively uses Colab for applications in TensorFlow, neural networks, exploring TPU, disseminating research, and creating tutorials. While the GPU is free, TPUs are provided by Google at $1.35 per hour. Google Collab offers 12 hours of execution time.

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Gradient, aka 1-click Jupyter Notebook, is a fully-configured notebook packed with all the necessary frameworks, libraries, and drivers. Gradient offers pre-configured templates or ML frameworks to hit the ground running. Any code can be launched using UI, CLI, or Github. Another highlight of Gradient is the real-time logs and graphs it builds while the model is being trained. The notebook supports online cloud data sources such as Amazon S3, Google Cloud Storage, and Microsoft Azure. The monthly plan is free for beginners, while GPUs and TPUs are available from $0.25 to $8.43 per hour. Gradient offers free GPUs for some instances.


A Jupyter-notebook enabled platform, Deepnote boasts of many advanced features. Deepnote supports real-time collaboration to discuss and debug the code. The platform will soon have functions such as versioning, code review, and reproducibility. Deepnote has intelligent features to quickly browse the code, find patterns in your data, and autocomplete code. It can integrate with Github, S3, PostgreSQL, and Google Big Query. The platform is free for beginners and is available for $12 for start-ups and small teams. The rates are dynamic for bigger teams. Deepnote does not currently support GPU or cloud-based resources.

Saturn Cloud

Saturn Cloud hosts Jupyter Notebooks and has seamless management capabilities for Python environments on the cloud. You can start a project by creating a Jupyter notebook and selecting the disk space and your machine’s size. The configurations meet the requirements for most of the practical data science projects. Automatic version control, customisable environments, and a cloud-hosted Jupyter allow for easy collaborations. The platform provides high scalability with different CPU and GPU plans. Pricing varies from $0.04 to $44 an hour, depending on the processing units and memory.

Apache Zeppelin

Apache Zeppelin is another web-based open-source notebook popular among data scientists. The platform supports three languages – SQL, Python, and R. Zeppelin also backs interpreters such as Apache Spark, JDBC, Markdown, Shell, and Hadoop. The built-in basic charts and pivot table structures help to create input forms in the notebook. Zeppelin can be shared on Github and offers resizable notebook cells as a unique interface feature.


Open-sourced by Netflix, Polynote is a notebook preferred for Scala. It supports the mixing of multiple languages in one notebook and allows easy data sharing. Since it shares the same file extension as Jupyter notebook, Polynote can be version controlled and displayed on Github. Thanks to editing features such as interactive autocomplete and rich text editing, the interface is highly user-friendly. Additionally, you can write equations in LaTex format, later converted into code. Polynote can be integrated with Apache Spark. The notebook also has an interface to see table-structured data and a built-in plot-editor to make data visualisation easy.

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