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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 FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsBook 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 projectsExplore deep learning, LLMs, and LLMOps to leverage generative AIUse Python to package your ML tools and scale up your solutionsGet to grips with Apache Spark, Kubernetes, and RayBuild and run ML pipelines with Apache Airflow, ZenML, and KubeflowDetect drift and build retraining mechanisms into your solutionsImprove error handling with control flows and vulnerability scanningHost and build ML microservices and batch processes running on AWSWho 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 EngineeringThe Machine Learning Development ProcessFrom Model to Model Factory Packaging UpDeployment Patterns and ToolsScaling UpDeep Learning, Generative AI, and LLMOps Building an Example ML MicroserviceBuilding an Extract, Transform, Machine Learning Use Case
From the Publisher
Practical problem-solving
Apply your acquired skills and knowledge to solve real-world problems with examples and hands-on exercises. You can use your practical knowledge to solve production issues and scale them to any size based on your needs.
Deep dive into the fundamentals
You’ll learn what good ML engineering processes look like. You’ll discover how to choose, train, and even automate your models. You’ll gain an understanding of what aspects of software engineering you should bring to ML, and explore all the newest libraries and tools available.
The book focuses on teaching you the fundamentals and introducing you to all the key aspects needed for a successful ML engineering career. You’ll explore best practices, tips, and tricks for software development.
Explore best practices for machine learning engineering Automate training and deployment for your ML processes Build wrapper libraries for encapsulating your data science and ML logic and solutions
Learn > Apply > Master
Learn like a professional
Cover the basics, learn what ML engineering is, how to do it well, and how to design your own process.
Start small, but go big
Put your knowledge to the test by training, deploying, and scaling your solutions.
Test yourself
Work through real-world end-to-end scenarios to really cement your new skills.
Get to grips with the biggest libraries and packages
This latest edition of Machine Learning Engineering with Python is packed with new information and techniques. Building on the solid foundation of the first edition, more technical depth has been introduced with the example chapters revamped completely.
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Price
$32.31$32.31 $43.99$43.99
Tools:
AWS + Open-Source Software AWS
Models:
Traditional ML models + LLMs + DLMs Traditional ML models
Approach:
How+Why How to
Chapters:
9 8
Publisher : Packt Publishing; 2nd ed. edition (August 31, 2023)
Language : English
Paperback : 462 pages
ISBN-10 : 1837631964
ISBN-13 : 978-1837631964
Item Weight : 1.76 pounds
Dimensions : 9.25 x 7.52 x 0.93 inches
