Paperback. Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers Intermediate-Advanced user level Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Bibliographic Details

Title: Deploy Machine Learning Models to Production (Paperback)
Publisher: APress, Berkley
Publication Date: 2020
Language: English
ISBN 10: 1484265459
ISBN 13: 9781484265451
Binding: Paperback
Edition: 1st Edition
Condition: new

About this title

Synopsis

Build and deploy machine learning and deep learning models in production with end-to-end examples.

This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.

The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.

What You Will Learn

Build, train, and deploy machine learning models at scale using Kubernetes
Containerize any kind of machine learning model and run it on any platform using Docker
Deploy machine learning and deep learning models using Flask and Streamlit frameworks

Who This Book Is For

Data engineers, data scientists, analysts, and machine learning and deep learning engineers

About the Author

Pramod Singh is Manager of Data Science at Bain & Company. Previously, he worked as Sr. Machine Learning Engineer at Walmart Labs and Data Science Manager at Publicis Sapient in India. He has spent over 10 years working in machine learning, deep learning, data engineering, algorithm design, and application development. He has authored three Apress books: Machine Learning with PySpark, Learn PySpark, and Learn TensorFlow 2.0. He is a regular speaker at major conferences such as O’Reilly’s Strata Data, GIDS, and other AI conferences. He is an active mentor and faculty in machine learning and AI at various educational institutes. He lives in Bangalore with his wife and four-year-old son. In his spare time, he enjoys playing guitar, coding, reading, and watching football.

Manager of Data Science at Bain & Company. He has over 11 years of experience in the data science field working with multiple product- and service-based organizations. He has been part of numerous ML and AI large-scale projects. He has published three books on large scale data processing and machine learning. He is a regular speaker at major AI conferences.

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