Machine Learning Engineering with Python, Second Edition

eBook Details:

• Paperback: 462 pages
• Publisher: WOW! eBook; 2nd edition (August 31, 2023)
• Language: English
• ISBN-10: 1837631964
• ISBN-13: 978-1837631964

eBook Description:

Machine Learning Engineering with Python, 2nd Edition: 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.

The Machine Learning Engineering with Python, Second Edition 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 Machine Learning Engineering with Python, 2nd 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.

• 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

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.