Machine Learning Engineering with Python - Manage the Lifecycle of Machine Learning Models Using MLOps with Practical Examples (2nd Edition)

This 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 concep...

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Hlavní autori: McMahon, Andrew P, Polak, Adi
Médium: E-kniha
Jazyk:English
Vydavateľské údaje: Birmingham Packt Publishing 2023
Packt Publishing, Limited
Packt Publishing Limited
Vydanie:2
Predmet:
COMPUTERS / Data Modeling & Design
COMPUTERS / Machine Theory
COMPUTERS / Neural Networks
Data mining
General Engineering & Project Administration
General References
Machine learning
Programming Languages
Python (Computer program language)
Software Engineering
ISBN:9781837631964, 1837631964
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Popis
Shrnutí:This 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.
ISBN:9781837631964
1837631964
DOI:10.0000/9781837634354