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. 
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. 
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.