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Machine Learning Engineering with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition

(ebook) (audiobook) (audiobook) Książka w języku 1
Machine Learning Engineering  with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition Andrew P. McMahon, Adi Polak - okladka książki

Machine Learning Engineering  with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition Andrew P. McMahon, Adi Polak - okladka książki

Machine Learning Engineering  with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition Andrew P. McMahon, Adi Polak - audiobook MP3

Machine Learning Engineering  with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition Andrew P. McMahon, Adi Polak - audiobook CD

Serie wydawnicze:
Hands-on
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
462
Dostępne formaty:
     PDF
     ePub
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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.

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O autorach książki

Andrew P. McMahon has spent years building high-impact ML products across a variety of industries. He is currently Head of MLOps for NatWest Group in the UK and has a PhD in theoretical condensed matter physics from Imperial College London. He is an active blogger, speaker, podcast guest, and leading voice in the MLOps community. He is co-host of the AI Right podcast and was named ‘Rising Star of the Year’ at the 2022 British Data Awards and ‘Data Scientist of the Year’ by the Data Science Foundation in 2019.

Adi Polak jest doświadczoną inżynierką, wiceprezeską do spraw programistów w firmie Treeverse, członkinią wielu grup eksperckich. Bierze udział w organizowaniu takich konferencji jak Data + AI Summit by Databricks, Current by Confluent i Scale by the Bay. Doświadczenie w uczeniu maszynowym zdobywała, prowadząc badania dla wielu firm z listy Fortune 500.

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