Book Image

Machine Learning Engineering with Python - Second Edition

By : Andrew P. McMahon
2.5 (2)
Book Image

Machine Learning Engineering with Python - Second Edition

2.5 (2)
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.
Table of Contents (12 chapters)
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*args 147

**kwargs 147, 148


abstraction 157

activation function 322

adaptive learning rate methods

AdaDelta 88

AdaGrad 88

Adam 88

RMSprop 88

advanced Airflow features

ETML pipeline, building with 405-417


versus Waterfall 51

Agile Manifesto

URL 49

Airflow DAG 75

Airflow role 242

reference link 242

Amazon CLI, configuring

reference link 215

Amazon SageMaker 246

Amazon Web Services (AWS) 39

Apache Airflow 232-235

CI/CD pipelines, setting up 244, 245

on AWS 235-243

used, for building pipelines 232

using, pros and cons 403

Apache Kafka 209

Apache Spark 206

Apache Storm 209


large language models (LLMs), consuming via 342-345


containerizing, with Docker 384, 385 378

Artifact store 247

artificial general intelligence (AGI) 341

artificial intelligence (AI) 319

Artificial Neural Networks...