Machine Learning Pipelines with Kubeflow on Amazon EKS

In Chapter 9, Security, Governance, and Compliance Strategies, we discussed a lot of concepts and solutions that focus on the other challenges and issues we need to worry about when dealing with machine learning (ML) requirements. You have probably realized by now that ML practitioners have a lot of responsibilities and work to do outside model training and deployment! Once a model gets deployed into production, we would have to monitor the model and ensure that we are able to detect and manage a variety of issues. In addition to this, ML engineers might need to build ML pipelines to automate the different steps in the ML life cycle. To ensure that we reliably deploy ML models in production, as well as streamline the ML life cycle, it is best that we learn and apply the different principles of machine learning operations (MLOps). With MLOps, we will make use of the tried-and-tested tools and practices from software engineering...