Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples

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 LangChain Key Features This 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 This 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. The book provides you with the skills you need to stay ahead in this rapidly evolving field. Machine Learning Engineering with Python adopts an example-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 your solutions. This edition delves deeper into 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'll learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You'll also explore AI assistants like GitHub Copilot to become more productive, and then understand the engineering considerations of working with deep learning. What you will learn Plan 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 youre not a developer but want to manage or understand the product lifecycle of these systems, youll also find this book useful. This book assumes 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 Contents Introduction 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