Design & Build an End-to-End Data Science Platform

Learn best practices and tools required to design and build an end-to-end machine learning platform.

Machine learning in production requires a data science platform that encompasses the complete set of tools that turn raw data into actionable intelligence. This research describes the best practices and tools that data scientists and data engineers can use to build a data science platform that combines existing data stores with cutting edge machine learning (ML) frameworks like TensorFlow. 

In this helpful guide, you will learn: 

  • How to adopt automated workflows and open source processing tools to save up to 60% of your time.
  • How moving to fully distributed environments (vs local environments) can help you reduce friction and increase agility when managing and training models.
  • The importance of collaboration across Data Engineers, DataOps, and Data Scientists to ensure the solution is easy to provision and manage day-to-day.

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