Get up and running with machine learning life cycle management and implement MLOps in your organization Key Features * Become well-versed with MLOps techniques to monitor the quality of machine learning models in production * Explore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed models * Perform CI/CD to automate new implementations in ML pipelinesBook DescriptionEngineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you’ll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability.
