This offering covers the end-to-end spectrum of ML services including data preparation, training, tuning, deploying, collaborating and sharing of machine learning models.

To succeed, leaders should select and manage AI projects with a thoughtful strategy driven by clear expectations, alignment to business goals, and iteration.

A Seattle startup is smoothing out the rough spots of putting trained machine learning programs into production.

Typical Azure Machine Learning Project Lifecycle (source: Microsoft). At the upcoming Visual Studio Live! @ Microsoft HQ 2025 conference in Redmond, Eric D. Boyd, founder and CEO of responsiveX, will ...