Moving to MLOps with AWS Sagemaker

As the machine learning space continues to develop, there is a growing need for simple, robust solutions to deploy highly complex ML workloads into production. Proof of Concepts are becoming a smaller part of the overall picture. These days, most of the time, data scientists know exactly what model will work for what use case, but even with this knowledge, delivering a solution can be messy for all those involved if not done with reusability in mind. MLOps, or Machine Learning Operations, are a set of best practices that enable simple, reliable model deployments and monitoring. MLOps can save you time, money, and allow for “closed loop” systems where models are constantly being updated as new data cascades through the process.

In this vlog we’ll go through how easy it is to leverage AWS Sagemaker to align with an MLOps suited framework. Regardless of where you are along your AI journey, this information will either be enlightening, or be a good brush up on best practices.

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