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Azure Machine Learning

Updated: May 23

Our Snippets Of Knowledge Weekly Blog Series

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As a data scientist, machine learning as a service is an attractive offering. With an easy-to-use graphical interface (the workspace/ML Studio) to perform experiments Azure Machine Learning delivers exactly this. Meaning focus and time can be spent on model creations that deliver business value, instead of costly platform setup. When provisioned Azure ML will create storage, a container registry, key store and application logging to support the workspace, avoiding all the infrastructure required.

Once the first round of experimentation is complete, Azure Machine Learning also supports the delivery life cycle by offering capabilities to build, deploy and manage models for downstream inference. With features aligned to machine learning operations (MLOps) concepts.

As with all Microsoft resources, responsible AI is baked in offering explainable, transparent governance for models. With a focus on fairness that can be assessed and exposed.

See MS Learn for more information on this Resource here.


We hope you found this knowledge snippet helpful.

Check out all our posts in this series here.

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