Book Image

Machine Learning Engineering with Python - Second Edition

By : Andrew P. McMahon
1 (1)
Book Image

Machine Learning Engineering with Python - Second Edition

1 (1)
By: Andrew P. McMahon

Overview of this book

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Introduction to ML Engineering
Introduction to ML Engineering
Technical requirements
Defining a taxonomy of data disciplines
Working as an effective team
ML engineering in the real world
What does an ML solution look like?
High-level ML system design
Summary
Free Chapter
2
The Machine Learning Development Process
The Machine Learning Development Process
Technical requirements
Setting up our tools
Concept to solution in four steps
Summary
3
From Model to Model Factory
From Model to Model Factory
Technical requirements
Defining the model factory
Learning about learning
Engineering features for machine learning
Designing your training system
Retraining required
Persisting your models
Building the model factory with pipelines
Summary
4
Packaging Up
Packaging Up
Technical requirements
Writing good Python
Choosing a style
Packaging your code
Building your package
Testing, logging, securing, and error handling
Not reinventing the wheel
Summary
5
Deployment Patterns and Tools
Deployment Patterns and Tools
Technical requirements
Architecting systems
Exploring some standard ML patterns
Containerizing
Hosting your own microservice on AWS
Building general pipelines with Airflow
Building advanced ML pipelines
Selecting your deployment strategy
Summary
6
Scaling Up
Scaling Up
Technical requirements
Scaling with Spark
Spinning up serverless infrastructure
Containerizing at scale with Kubernetes
Scaling with Ray
Designing systems at scale
Summary
7
Deep Learning, Generative AI, and LLMOps
Deep Learning, Generative AI, and LLMOps
Going deep with deep learning
Living it large with LLMs
Building the future with LLMOps
Summary
8
Building an Example ML Microservice
Building an Example ML Microservice
Technical requirements
Understanding the forecasting problem
Designing our forecasting service
Selecting the tools
Training at scale
Serving the models with FastAPI
Containerizing and deploying to Kubernetes
Summary
9
Building an Extract, Transform, Machine Learning Use Case
Building an Extract, Transform, Machine Learning Use Case
Technical requirements
Understanding the batch processing problem
Designing an ETML solution
Selecting the tools
Executing the build
Summary
10
Other Books You May Enjoy
Other Books You May Enjoy
11
Index
Index

Retraining required

You wouldn't expect that after finishing your education, you never read a paper or book or speak to anyone again, which means you wouldn't be able to make informed decisions about what is happening in the world. So, you shouldn't expect a ML model to be trained once and then be performant forever afterward.

This idea is intuitive, but it represents a formal problem for ML models known as drift. Drift is a term that covers a variety of reasons for your model's performance dropping over time. It can be split into two main types:

  1. Concept drift: This happens when there is a change in the fundamental relationship between the features of your data and the outcome you are trying to predict. Sometimes, this is also known as covariate drift. An example could be that at the time of training, you only have a subsample of data that seems to show a linear relationship between the features and your outcome. If it turns out that, after gathering a lot more data...
Previous Section
End of Section 2
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