Machine Learning Engineering with Python - Second Edition: Manage the lifecycle

<p><strong>Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems</strong></p><p><br></p><p><strong>Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain</strong></p><p><br></p><p><strong>Key Features: </strong></p><p><br></p><ul><li>This second edition delves deeper into key machine learning topics, CI/CD, and system design</li><li>Explore core MLOps practices, such as model management and performance monitoring</li><li>Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools</li></ul><p><br></p><p><strong>Book Description: </strong></p><p><br></p><p>Machine Learning Engineering with Python, 2nd Edition, is the practical guide that MLOps and ML engineers need to build robust solutions to solve real-world problems, providing you with the skills and knowledge you need to stay ahead in this rapidly evolving field.</p><p><br></p><p>The book takes a hands-on, examples-focused approach providing essential technical concepts, implementation patterns, and development methodologies. You'll go from understanding the key steps of the machine learning development lifecycle to building and deploying robust machine learning solutions. Once you've mastered the basics, you'll get hands-on with deployment architectures and discover methods for scaling up your sol