Platform and Model Design for Responsible AI

6

Hyperparameter Tuning, MLOps, and AutoML

Developing a Machine Learning (ML) model is an iterative process; the presence of so many models, each with a large number of hyperparameters, complicates things for beginners. This chapter continues from the previous chapter and explains the need for continuous training in ML pipelines. It will provide a glimpse of the AutoML options currently available for your ML workflow, expand on the situations in which no-code/low-code solutions are useful, and explore the solutions provided by major cloud providers in terms of their ease of use, features, and model explainability. This chapter will also explore orchestration tools such as Kubeflow and Vertex AI, which you can use to manage the continuous training ...