MLOps (“machine learning” and “operations”) and AI at Scale

In the past years many organisations invested in AI/Machine Learning experiments, most of them were not deployed to production.

To push a successful AI/ML experiment to production the organisation need to understand about MLOps.

What is MLOps?

MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle. [1]

MLOps empowers data scientists and app developers to help bring ML models to production. MLOps enables you to track / version / audit / certify / re-use every asset in your ML lifecycle and provides orchestration services to streamline managing this lifecycle. [2]

How does Azure ML help with MLOps?

Azure ML contains a number of asset management and orchestration services to help you manage the lifecycle of your model training & deployment workflows.

With Azure ML + Azure DevOps you can effectively and cohesively manage your datasets, experiments, models, and ML-infused applications. [2]

Reference
[1] https://en.wikipedia.org/wiki/MLOps
[2] https://github.com/Microsoft/MLOps