Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems
Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChainKey FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system design
Explore core MLOps practices, such as model management and performance monitoring
Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools

Book Description
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.What you will learnPlan and manage end-to-end ML development projects
Explore deep learning, LLMs, and LLMOps to leverage generative AI
Use Python to package your ML tools and scale up your solutions
Get to grips with Apache Spark, Kubernetes, and Ray
Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow
Detect drift and build retraining mechanisms into your solutions
Improve error handling with control flows and vulnerability scanning
Host and build ML microservices and batch processes running on AWS

Who this book is for
This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you're not a developer but want to manage or understand the product lifecycle of these systems, you'll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.Table of ContentsIntroduction to ML Engineering
The Machine Learning Development Process
From Model to Model Factory
Packaging Up
Deployment Patterns and Tools
Scaling Up
Deep Learning, Generative AI, and LLMOps
Building an Example ML Microservice
Building an Extract, Transform, Machine Learning Use Case