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    Machine Learning Engineering with Python: Manage the production life cycle of machine learning models

    Posted By: yoyoloit
    Machine Learning Engineering with Python: Manage the production life cycle of machine learning models

    Machine Learning Engineering with Python
    by Andrew P. McMahon

    English | 2021 | ISBN: ‎ 1801079250 | 277 pages | True (PDF EPUB) | 32.14 MB

    Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments
    Key Features

    Explore hyperparameter optimization and model management tools
    Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages
    Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases

    Book Description

    Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.

    Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems.

    By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.