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Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, youâÂÂll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersâÂÂincluding experienced practitioners and novices alikeâÂÂwill learn the tools, best practices, and challenges involved in building a real-world ML application step by step.
Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:
Define your product goal and set up a machine learning problem
Build your first end-to-end pipeline quickly and acquire an initial dataset
Train and evaluate your ML models and address performance bottlenecks
Deploy and monitor your models in a production environment
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, youâÂÂll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersâÂÂincluding experienced practitioners and novices alikeâÂÂwill learn the tools, best practices, and challenges involved in building a real-world ML application step by step.
Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:
Define your product goal and set up a machine learning problem
Build your first end-to-end pipeline quickly and acquire an initial dataset
Train and evaluate your ML models and address performance bottlenecks
Deploy and monitor your models in a production environment
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