Data Science in Production
Data Science in Production
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Data Science in Production

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Completed on 2020-01-01

About the Book

Putting machine learning models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. 

In addition to discussing tools and libraries for scalable Python, this book covers DevOps issues that become part of the data science discipline when building production systems. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production.This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: 

  • Translate models developed on a laptop to scalable deployments in the cloud     
  • Develop end-to-end systems that automate data science workflows 
  • Own a data product from conception to production to sunset 

The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries.

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About the Author

Ben G Weber
Ben G Weber

Ben Weber is a distinguished data scientist at Zynga with past experience at Twitch, Electronic Arts, Daybreak Games, and Microsoft Studios.

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