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Effective Data Science Infrastructure: How to make data scientists productive (MEAP v07)
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Simplify data science infrastructure to give data scientists an efficient path from prototype to production. Effective Data Science Infrastructure 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. As you work through this easy-to-follow guide, you’ll set up end-to-end infrastructure from the ground up, with a fully customizable process you can easily adapt to your company. 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. Throughout, you’ll follow a human-centric approach focused on user experience and meeting the unique needs of data scientists.
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
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
年:
2022
出版:
1 / MEAP v07
出版社:
Manning Publications
语言:
english
页:
356
ISBN 10:
1617299197
ISBN 13:
9781617299193
文件:
PDF, 11.43 MB
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IPFS:
CID , CID Blake2b
english, 2022
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