The Machine Learning Solutions Architect Handbook - Second Edition: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
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- Автор: David Ping
- ISBN-10: 1805122509
- ISBN-13: 978-1805122500
- Publisher: Packt Publishing
- Publication date: April 15, 2024
- Language: English
- Dimensions: 9.25 x 7.52 x 1.22 inches
- Print length: 602 pages
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Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.
Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.
We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.
From the Publisher
What's new in this edition?
To succeed as an ML practitioner, an ML solutions architect must not only deepen their expertise across various facets of ML but also continuously broaden their knowledge beyond the purely technological realm. In this new edition, we will explore cutting-edge technology topics such as the generative AI lifecycle, generative AI technology foundations and platforms, industry generative AI solutions, and AI risk and cybersecurity. Furthermore, we will delve into expanded AI topics, including ML maturity, ML journey strategy and implementation – encompassing use case ideation and selection, technology platforms, business adoption, and more. This edition will also provide updates to many topics from the first edition, such as ML algorithms, data management, open-source tools, AWS services, and ML platform architecture, among others.
Key topics:
- ML libraries, ML algorithms, and enterprise ML platforms
- Architectural framework of a data science environment
- Inference engine optimization and inference in LLMs
- MLOps architecture design for AI services
- ML platform design for governance and AI risk management
- ...and more!
How has your experience in the industry helped you write this book?
With over 25 years of experience in technology and organizational leadership across enterprise technology, cloud computing, and AI/ML, I have had the privilege of advising global companies, technology leaders, and practitioners on their cloud and AI/ML journeys. This spans various industries, including financial services, life sciences, media and entertainment, automotive, telecommunications, and energy sectors. Through these engagements, I have gained hands-on knowledge in the business and technological perspectives driving the adoption of AI/ML solutions in these industries. I have witnessed, first-hand, the vital skills an ML solutions architect must develop to be an effective participant in the rapidly evolving AI/ML field. Much of this learning has been applied to this book, ensuring that it provides an exhaustive and practical guide for professionals in this field.
What's your favorite part in the book?
One of my favorite parts of this book are the chapters that focus on generative AI. Keeping pace with the swift advancements in this field has been both intellectually stimulating and challenging. Over the past year, I have had the privilege of collaborating with many organizations on their generative AI initiatives and it has been remarkable to witness these diverse organizations harness the power of this disruptive technology in real-world scenarios. The opportunity to document my own experiences and insights gained from working with generative AI has been immensely rewarding. It has allowed me to to distill and share my perspectives on this transformative technology with like-minded readers.
| The Machine Learning Solutions Architect Handbook - Second Edition | Machine Learning Engineering with Python - Second Edition | |
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| Intended audience | ML solutions architects and those interested in ML project architecture and operations. | Developers, data scientists, and engineers looking to deepen their expertise in ML engineering. |
| ML lifecycle | Dissect the ML lifecycle, from architecture to operations and from business ideation to deployment. | Develop an end-to-end process, from managing ML projects to deploying production-grade ML systems. |
| Practical implementation | Delve into ML use cases and solutions across industries using AWS services and open-source libraries. | Create reusable ML pipelines, packages, and deployment patterns using open-source tools and AWS. |
| Generative AI | Navigate the generative AI project lifecycle with practical considerations for scalable deployment. | Build a simple LLM pipeline using LangChain. |
| System design | Design system architecture for ML solutions, covering workflow automation, security, and compliance. | Create reusable training and monitoring pipelines, showcasing various deployment architectures. |