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Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform de Pramod Singh

Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform

Autor Pramod Singh
en Limba Engleză Paperback – 15 dec 2020
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

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Specificații

ISBN-13: 9781484265451
ISBN-10: 1484265459
Pagini: 150
Ilustrații: XIII, 150 p. 115 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.24 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

Chapter 1: Introduction to Machine Learning.- Chapter 2: Model Deployment and Challenges.- Chapter 3: Machine Learning Deployment as a Web Service.- Chapter 4: Machine Learning Deployment Using Docker.- Chapter 5: Machine Learning Deployment Using Kubernetes.



Notă biografică

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.

Textul de pe ultima copertă

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 examplesand extend them in interesting ways.

You will:
  • 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


Caracteristici

Guides you in transitioning from traditional machine learning to machine learning productionization Covers the entire range of deployment options, including Flask, Streamlit, Docker, and Kubernetes Presents the process to wrap and containerize any machine learning model