Machine Learning Platform Engineering by Benjamin Tan Wei Hao (.PDF)
File Size: 30.3 MB
Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems (Final Release) by Benjamin Tan Wei Hao, Shanoop Padmanabhan, Varun Mallya
Requirements: .PDF reader, 30.3 MB | True PDF
Overview: Get your Machine Learning models out of the lab and into production! Delivering a successful Machine Learning project is hard. Machine Learning Platform Engineering makes it easier. Machine Learning Platform Engineering shows you how to build an effective IDP for ML and AI applications. Each chapter illuminates a vital part of the ML workflow, including setting up orchestration pipelines, selecting models, allocating resources for training, inference, and serving, and more. As you go, you’ll create a versatile modern platform using open source tools like Kubeflow, MLFlow, BentoML, Evidently, Feast, and LangChain. This book is for data scientists and software engineers who want to move beyond Jupyter Notebooks to production ML systems. You should be comfortable with Python and have basic familiarity with ML concepts. No prior experience with Docker, Kubernetes, or Machine Learning operations (MLOps) tools is required—we’ll build everything from scratch. Experienced ML practitioners will benefit from the systematic approach to infrastructure and the modern LLMOps coverage in the final chapters.
Genre: Non-Fiction > Tech & Devices
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