Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples

Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-World problemsIncludes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChainKey FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools