9781492045113 - Building Machine Learning Powered Applications: Going from Idea to Product by Ameisen, Emmanuel (40 results)
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- Softcover
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- Softcover
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Paperback or Softback. Condition: New. Building Machine Learning Powered Applications: Going from Idea to Product. Book.
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Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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Paperback. Condition: New. Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-including exper…ienced practitioners and novices alike-will 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 problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment.
- Softcover
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- Softcover
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- Softcover
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- Softcover
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Paperback. Condition: new. Paperback. Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-incl…uding experienced practitioners and novices alike-will 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 problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy 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, youll build an example ML-driven application from initial idea to deployed product. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Paperback. Condition: New. Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-including exper…ienced practitioners and novices alike-will 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 problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment.
- Softcover
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Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
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- Softcover
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Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 238 pages. 9.00x6.75x0.25 inches. In Stock.
- Softcover
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- Softcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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Condition: New. 2020. Paperback. . . . . .
- Softcover
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Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
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Paperback. Condition: New. Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-including exper…ienced practitioners and novices alike-will 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 problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment.
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- Softcover
Seller: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, GermanyRheinberg-Buch Andreas Meier eK
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Taschenbuch. Condition: Neu. Neuware -Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersinclu…ding experienced practitioners and novices alikewill 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 Englisch.
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. Neuware -Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersinclu…ding experienced practitioners and novices alikewill 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 Englisch.
- Softcover
Seller: Speedyhen, Hertfordshire, United KingdomSpeedyhen
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Seller: Wegmann1855, Zwiesel, GermanyWegmann1855
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Taschenbuch. Condition: Neu. Neuware -Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersinclu…ding experienced practitioners and novices alikewill 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.
