This book equips you with the essential skills to navigate the swiftly evolving landscape of machine learning (ML) and provides a practical, examples-based approach to skill development. Covering technical concepts, implementation patterns, and development methodologies, it guides you through the ML development lifecycle, assisting you in establishing your standardized “model factory” for effective training and retraining of models. The text delves into crucial aspects such as continuous integration/continuous deployment (CI/CD), the identification of different drift types, and hands-on exploration of the latest deployment architectures and scalability solutions.
In this edition, there's a comprehensive exploration of ML engineering and MLOps, emphasizing the integration of cutting-edge open-source and cloud-based technologies. The content includes a restructured approach to advanced pipelining and orchestration techniques. A new chapter on deep learning, generative AI, and LLMOps introduces tools like LangChain, PyTorch, and Hugging Face for leveraging large language models (LLMs) in advanced analysis. The book also delves into AI assistants like GitHub Copilot to enhance productivity, followed by an in-depth exploration of engineering considerations when working with deep learning.
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