Effective Data Science Infrastructure: How To Make Data Scientists Productive

Simplify data science infrastructure to give data scientists an efficient path from prototype to production.

Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems.

In Effective Data Science Infrastructure you will learn how to:

– Design data science infrastructure that boosts productivity
– Handle compute and orchestration in the cloud
– Deploy machine learning to production
– Monitor and manage performance and results
– Combine cloud-based tools into a cohesive data science environment
– Develop reproducible data science projects using Metaflow, Conda, and Docker
– Architect complex applications for multiple teams and large datasets
– Customize and grow data science infrastructure

Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python.

The author is donating proceeds from this book to charities that support women and underrepresented groups in data science.

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