The Machine Learning Solutions Architect Handbook
Written by David Ping, this book is an essential guide for anyone looking to delve deep into the intricacies of machine learning architecture. With practical strategies and best practices outlined for the entire machine learning lifecycle, it covers vital aspects such as system design, MLOps, and the increasingly significant realm of generative AI. Ping’s expert insights provide readers with a comprehensive understanding that balances theoretical foundations with practical applications. This text is perfect for aspiring machine learning architects and professionals seeking to enhance their skills.
Learning Google Cloud Vertex AI
Kumar K, Hemanth presents a thorough introduction to machine learning models within the Google Cloud ecosystem in this must-have book. “Learning Google Cloud Vertex AI” not only equips you to build and deploy models but also empowers you to manage them effectively. This book is perfect for both beginners and seasoned professionals aiming to leverage the power of Google Cloud’s Vertex AI and offers hands-on guidance to ensure your learning journey is actionable and effective.
Deploy Machine Learning Models to Production
Singh, Pramod offers an in-depth exploration of deploying machine learning models via a comprehensive approach using Flask, Streamlit, Docker, and Kubernetes. This text is pivotal for data scientists and developers keen on taking their ML projects from development environments to real-world applications on the Google Cloud Platform. The practical insights and step-by-step recipes detailed in this book make it a worthwhile investment for those looking to master the deployment aspect of machine learning.
Machine Learning with Amazon SageMaker Cookbook
For readers eager to experiment with machine learning, Joshua Arvin Lat’s “Machine Learning with Amazon SageMaker Cookbook” is a veritable treasure trove. This cookbook presents 80 proven recipes tailored for data scientists and developers, facilitating an efficient approach to conducting experiments and deploying models. With practical examples and covering a wide range of topics, this book serves to fast-track your understanding and execution of machine learning projects using Amazon SageMaker.
IoT Edge Computing with MicroK8s
Shanmugam, Karthikeyan’s insightful book highlights the significance of understanding IoT architecture and deployments in today’s technology landscape. The hands-on approach detailed in “IoT Edge Computing with MicroK8s” guides readers through building, deploying, and managing production-ready Kubernetes in IoT and Edge environments. This book is a valuable resource for those looking to delve into the convergence of IoT and cloud technologies while building robust real-world applications.
Scaling Up Machine Learning with MLOps
In the fluid and ever-changing domain of machine learning, doitsu offers vital perspectives in the Japanese Edition of “Scaling Up Machine Learning with MLOps.” This text navigates optimal solutions that aim to make results sustainable. It serves as a fundamental resource for those interested in the operational aspects of machine learning, promoting systemic approaches that enhance scalability and efficiency across various ML applications. A must-read for practitioners keen on integration and optimization in ML workflows.
