[center]Machine Learning Engineering with Python (for . .) | Andrew P. McMahon | Packt | [/align]

Machine learning engineering is an in-demand skill set, and it can be difficult to find a helpful guide on the topic. This fully updated second edition will help you solve business problems by addressing the pain points in creating standardized pipelines for taking proof-of-concept ML models to production and producing trustworthy results.
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.

Contents of download скачать:
Machine Learning Engineering with Python Manage the lifecycle of machine learning models using MLOps with practical examples.epub (18.55 MB)

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