Running machine learning (ML) experiments in the cloud can span across many services and components. The ability to structure, automate, and track ML experiments is essential to enable rapid development of ML models. With the latest advancements in the field of automated machine learning (AutoML), namely the area of ML dedicated to the automation of ML processes, you can build accurate decision-making models without needing deep ML knowledge. In this post, we loo at AutoGluon, an open-source AutoML framework that allows you to build accurate ML models with just a few lines of Python.
AWS offers a wide range of services to manage and run ML workflows, allowing you to select a solution based on your skills and application. For example, if you already use AWS Step Functions to orchestrate the components of distributed applications, you can use the same service to build and automate your ML workflows. Other MLOps

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