Document ID: CGBN123 | Last Updated: May 4, 2018
In the context of Machine Learning (ML) and AI, data scientists are the ones who figure out how to apply ML algorithms and approaches to data to gain the insights necessary to power intelligent applications. As such, data scientists, and an emerging new class of ML engineers and scientists, need tools that help them access the functionality and infrastructure necessary to execute their data science and ML needs. This has resulted in the emergence of a class of Data Science Platforms and related tools. Anaconda is the most widely implemented Python data science platform with over 6 million users, popularizing and supporting open source projects including NumPy and SciPy, as well as their Anaconda Enterprise, Cloud, and Repository offerings. Anaconda’s primary differentiators are strength in deployment and developer operations (“devops”) with good integration with enterprise developer tools and ecosystems.