This is a practical guide to building ML solutions to real-world problems. Andrew P. McMahon takes an examples-based approach and covers technical concepts, implementation patterns, and development methodologies. This edition has a new chapter on deep learning, generative AI, and LLMOps, and covers tools like LangChain, PyTorch, and Hugging Face to use LLMs for analysis. The book also looks at AI assistants like GitHub Copilot.
<ASIN:1837631964>
Author: Andrew P. McMahon Publisher: Packt Publishing Date: August 2023 Pages: 462 ISBN: 9781837631964 Print:1837631964 Kindle:B0BXXQJC2D Audience: Python developers interested in machine learning Level: Intermediate Category: Artificial Intelligence
Topics covered:
- 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
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Artificial Intelligence, Machine Learning, and Deep Learning (Mercury Learning)
Author: Oswald Campesato Publisher: Mercury Learning Date: February 2020 Pages: 300 ISBN: 978-1683924678 Print: 1683924673 Kindle: B084P1K9YP Audience: Developers interested in machine learning Rating: 4 Reviewer: Mike James
Another AI/ML book - is there room for another one?
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Bare Metal C
Author: Steve Oualline Publisher: No Starch Press Date: August 2022 Pages: 304 ISBN: 978-1718501621 Print: 1718501625 Kindle: B08YJB9BCF Audience: C programmers Rating: 3 Reviewer: Harry Fairhead Bare metal C sounds exciting and very basic. Time to find out how the machine really works.
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