Data Science and machine learning have never been more popular. With the growth of the field, comes the maturation of the entire spectrum of tools available for practitioners today.
A notable welcome has been the emergence of a wide variety of new tools, startups, and entire categories aimed at solving specific problems faced by practitioners and organizations. In this infographic, we provide an overview of the tools landscape in data science and machine learning in 2022.
Below, you will find a detailed overview of the tools mentioned in the infographic above.
A great advancement in the state of tooling over the past few years has been the arrival of many tools that allow practitioners to manage data better for data science and machine learning workflows. These range from synthetic data generation tools that allow for generating data, data observability tools that monitor data pipelines in production, data versioning tools that provide version control over data, data pipelining tools and orchestration tools that let practitioners orchestrate workflows, data catalogs that showcase the organization’s data for consumption, and more.
- Alex Solutions
- Google Cloud Data Catalog
End-to-End Machine Learning Platforms
Machine learning platforms are inching to become the norm. These platforms provide the ability to do end-to-machine learning from feature processing to deployment, with certain tools providing the ability for automated machine learning and deployment.
Within the data science ecosystem, falls a plethora of tools ranging from Notebooks & IDEs, data analysis packages and software, data visualization, feature stores for storing features used in machine learning, deep learning and machine learning libraries, and hyperparameter optimization libraries, model debugging tools, and more.
Notebooks & IDEs
- Google Colab
- Amazon SageMaker Studio Lab
- DataCamp Workspace
- Power BI
- Google Data Studio
- IBM Cognos
Machine Learning Frameworks
Deep Learning Frameworks
The past two years have seen the rise of MLOps and the importance of deploying machine learning models in production. This has spurred the development and evolution of tools that allow practitioners to package models into applications, monitor models in production, track experiments at scale, and serve models into production.
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