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Machine Learning Engineering with Python

Machine Learning Engineering with Python

By : Andrew P. McMahon
4.9 (21)
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Machine Learning Engineering with Python

Machine Learning Engineering with Python

4.9 (21)
By: Andrew P. McMahon

Overview of this book

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.
Table of Contents (13 chapters)
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1
Section 1: What Is ML Engineering?
4
Section 2: ML Development and Deployment
9
Section 3: End-to-End Examples

Executing the build

Execution of the build, in this case, will be very much about how we take the Proof-Of-Concept code shown in Chapter 1, Introduction to ML Engineering, and then split this out into components that can be called by another scheduling tool such as Apache Airflow. This will provide a showcase of how we can apply the skills we learned in Chapter 4, Packaging Up.

In the next few sections, we will walk through how to inject some engineering best practices into the code base, and we will discuss some coding examples to help bring this to reality. We will not focus on the scheduling and pipelining aspect for Apache Airflow (please refer to Chapter 5, Deployment Patterns and Tools, for this) but will focus instead on how some simple adaptations to an existing code base can dramatically improve its production readiness.

Not reinventing the wheel in practice

As discussed in Chapter 3, From Model to Model Factory, whether we run our ML pipeline in a train-run or train...

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Programming languages
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Machine Learning Engineering with Python
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