Build a Machine Learning Platform (from Scratch)
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Get your machine learning models out of the lab and into production! Delivering a successful machine learning project is hard. Build a Machine Learning Platform (From Scratch) makes it easier. In it, you’ll design a reliable ML system from the ground up, incorporating MLOps and DevOps along with a stack of proven infrastructure tools including Kubeflow, MLFlow, BentoML, Evidently, and Feast. In Build a Machine Learning Platform (From Scratch) you’ll learn how to: • Set up an MLOps platform • Deploy machine learning models to production • Build end-to-end data pipelines • Ef...
Get your machine learning models out of the lab and into production! Delivering a successful machine learning project is hard. Build a Machine Learning Platform (From Scratch) makes it easier. In it, you’ll design a reliable ML system from the ground up, incorporating MLOps and DevOps along with a stack of proven infrastructure tools including Kubeflow, MLFlow, BentoML, Evidently, and Feast. In Build a Machine Learning Platform (From Scratch) you’ll learn how to: • Set up an MLOps platform • Deploy machine learning models to production • Build end-to-end data pipelines • Effective monitoring and explainability A properly designed machine learning system streamlines data workflows, improves collaboration between data and operations teams, and provides much-needed structure for both training and deployment. In Build a Machine Learning Platform (From Scratch) you’ll learn how to design and implement a machine learning system from the ground up. You’ll appreciate this instantly-useful introduction to achieving the full benefits of automated ML infrastructure. About the book Build a Machine Learning Platform (From Scratch) teaches you to set up and run a production-quality machine learning system using open source tools. Chapter-by-chapter, you’ll assemble a delivery pipeline for an image classifier and a recommendation system, learning best practices as you go. You’ll get hands-on experience with the most important parts of the machine learning workflow, including orchestrating pipelines; model training, inference, and serving; and monitoring and explainability. Soon, you’ll be deploying models that are fast to production and easy to maintain and scale. About the reader For data scientists or software engineers who know how to program in Python. About the author Benjamin Tan is a product manager and principal engineer for sata Science at DKatalis where he leads a team of talented machine learning engineers, data scientists, and data engineers. He is also the author of The Little Elixir and OTP Guidebook and Building an ML Pipeline with Kubeflow (liveProject) from Manning, and Mastering Ruby Closures. Shanoop Padmanabhan is a software engineering manager at Continental Automotive, where he leads a team of software engineers focusing on machine learning based perception for autonomous vehicles. Varun Mallya is a machine learning engineer working at DKatalis where he is responsible for the setup and maintenance of the Bank’s machine learning platform. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
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