Book : Machine Learning Engineering With Python Manage The..

-Titulo Original : Machine Learning Engineering With Python Manage The Production Life Cycle Of Machine Learning Models Using Mlops With Practical Examples

-Fabricante :

Packt Publishing

-Descripcion Original:

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. Youll 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, youll 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, youll work through examples to help you solve typical business problems. By the end of this book, youll 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. What you will learn Find out what an effective ML engineering process looks like Uncover options for automating training and deployment and learn how to use them Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions Understand what aspects of software engineering you can bring to machine learning Gain insights into adapting software engineering for machine learning using appropriate cloud technologies Perform hyperparameter tuning in a relatively automated way Who this book is for This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If youre someone who manages or wants to understand the production life cycle of these systems, youll find this book useful. Intermediate-level knowledge of Python is necessary. Table of Contents Introduction to ML Engineering The Machine Learning Development Process From Model to Model Factory Packaging Up Deployment Patterns and Tools Scaling Up Building an Example ML Microservice Building an Extract Transform Machine Learning Use Case Review ML Ops is one of the hottest topics in analytics and data science at the moment. This book does a great job of providing a useful overview of the subject and really practical examples of how to do it in anger. If you are a data scientist or want to be one you should buy this book and read it before your next interview or big team meeting. -Zachary Anderson, Chief Data and Analytics Officer, NatWest Group If you want to know how to take ML from applying the theory to actually doing it for real this book provides an excellent roadmap. For industry practitioners, this is going to become a required text and for academics like myself, this provides a detailed overview of the extra bits that we need to get students to consider in our courses. I cannot recommend this book highly enough! -Gordon Morrison, Professor of Data Science and Head of Computing Department at Glasgow Caledonian University About the Author Andrew Peter (Andy) McMah