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Machine Learning Engineering with Python - Manage the production life cycle of machine learning models using MLOps with practical examples (Paperback)
Loot Price: R1,398
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Machine Learning Engineering with Python - Manage the production life cycle of machine learning models using MLOps with practical examples (Paperback)
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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 DescriptionMachine 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.
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 forThis book is for
machine learning engineers, data scientists, and software
developers who want to build robust software solutions with machine
learning components. If you're someone who manages or wants to
understand the production life cycle of these systems, you'll find
this book useful. Intermediate-level knowledge of Python is
necessary.
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