Turn your machine learning projects into successful deployments with this practical guide to building and scaling solutions that solve real-world problems.
Includes a new chapter on generative AI and large language models (LLM) and building a pipeline that leverages LLM using LangChain.
1. Main features:
✓ This second edition goes deeper into key topics of machine learning, CI/CD and system design.
✓ Explore core MLOps practices, such as model management and performance monitoring.
✓ Build complete examples of deployable ML microservices and pipelines using AWS and open source tools.
2. Book description:
This second edition of Machine Learning Engineering with Python is the practical guide MLOps and ML engineers need to build solutions for real-world problems. The book gives you the skills you need to stay ahead in this rapidly growing field.
Machine Learning Engineering with Python takes an example-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methods you need. You will 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 how to use concepts like CI/CD and how to detect different types of bias.
Get hands-on with the latest deployment architectures and explore methods to scale your solution. This edition dives deeper into all aspects of ML engineering and MLOps, focusing on the latest open source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.
With new chapters on deep learning, generative AI, and LLMOps, you’ll learn how to use tools like LangChain, PyTorch, and Hugging Face to leverage LLM for supercharged analysis. You’ll also explore AI assistants like GitHub Copilot to work more efficiently and then understand the technical considerations when working with deep learning.
3. What you will learn:
✓ Plan and manage ML development projects from start to finish.
✓ Explore deep learning, LLM and LLMOps to leverage generative AI.
✓ Use Python to package ML tools and scale your solutions.
✓ Get familiar with Apache Spark, Kubernetes and Ray.
✓ Build and run ML pipelines with Apache Airflow, ZenML and Kubeflow.
✓ Detect deviations and build retraining mechanisms into your solution
✓ Improve error handling with control structures and vulnerability scanning.
✓ Host and build ML microservices as well as batch processes running on AWS.
4. Who is this book for:
This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build powerful solutions that use machine learning to solve real-world problems. If you are not a developer but want to manage or understand the product lifecycle of these systems, you will also find this book useful. This book assumes basic knowledge of machine learning concepts and intermediate programming experience in Python. With a focus on practical skills and real-world examples, this book is an essential resource for anyone wanting to advance their machine learning engineering career.
5. Table of contents:
✓ Chapter 1: Introduction to ML Engineering.
✓ Chapter 2: Machine Learning Development Process.
✓ Chapter 3: From Model to Model Factory.
✓ Chapter 4: Packaging Up.
✓ Chapter 5: Deployment Patterns and Tools.
✓ Chapter 6: Scaling Up.
✓ Chapter 7: Deep Learning, Generative AI, and LLMOps.
✓ Chapter 8: Building a sample ML Microservice.
✓ Chapter 9: Building an Extract, Transform, Machine Learning Use Case.
Link download : https://drive.google.com/drive/folders/1E59NLMEIsi4t2ffbGShvQ30uDCon9GFn
Password open file : https://drive.google.com/drive/folders/1lna5LnxuP3DTN6hx-cMYuYdzNboyIhIB
