Implementing MLOps in the Enterprise (Second Early Release)

[LeeAndro]

English | 2022 | ISBN: 9781098136574 | 81 pages | True EPUB, MOBI | 14.04 MB

With demand for scaling, real- access, and other capabilities, businesses need to consider building operational machine learning pipelines.​

This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps eeers, and machine learning eeers will learn how to tackle challenges that prevent many businesses from moving ML models to production.

Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than bning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.

You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you

Learn the MLOps process, including its technological and business value
Build and structure effective MLOps pipelines
Efficiently scale MLOps across your organization
Explore common MLOps use cases
Build MLOps pipelines for hybrid deployments, real- predictions, and composite AI
Learn how to prepare for and adapt to the future of MLOps
Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy



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