Earlier today, we disclosed a set of major updates to Azure Machine Learning designed for data scientists to build, deploy, manage, and monitor models at any scale. This has been in private preview for the last 6 months, with over 100 companies, and we’re incredibly excited to share these updates with you today. This post covers the learnings we’ve had with Azure Machine Learning so far, the trends we’re seeing from our customers today, the key design points we’ve considered in building these new features, and dive into the new capabilities.
We launched Azure Machine Learning Studio three years ago, designed to enable established data scientists and those new to the space to easily compose and deploy ML models. Before the term was in use, we enabled serverless training of experiments built by graphically composing from a rich set of modules, and then deploying these as a web service with the push of a button. The service serves billions of scoring requests on top of hundreds of thousands of models built by data scientists.
Read about it in the Azure blog.