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Get your machine learning models out of the lab and into production!
Delivering a successful machine learning project is hard. Design a Machine Learning System (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 Design a Machine Learning System (From Scratch) you’ll learn how to:
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 Design a Machine Learning System (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.
Delivering a successful machine learning project is hard. Design a Machine Learning System (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 Design a Machine Learning System (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 Design a Machine Learning System (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
Design a Machine Learning System (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 authors
Benjamin Tan is a Product Manager and Principal Engineer for Data 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.
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choose your plan
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Design a Machine Learning System (From Scratch) ebook for free
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$49.99
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- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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Design a Machine Learning System (From Scratch) ebook for free
