Intelligent experiences powered by AI can seem like magic to users. Developing them, however, is cumbersome involving a series of sequential and interconnected decisions along the way that are pretty time consuming.
What if there was an automated service that identifies the best machine learning pipelines for a given problem/data? Automated machine learning does exactly that! Automated ML is based on a breakthrough from our Microsoft Research division. The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous space of possible machine learning pipelines intelligently and efficiently. It's essentially a recommender system for machine learning pipelines. Like how streaming services recommend movies for users, automated ML recommends machine learning pipelines for data sets.
Just as important, automated ML accomplishes all this without having to see the customer’s data, preserving privacy. Automated ML is designed to not look at the customer’s data. Customer data and execution of the machine learning pipeline both live in the customer’s cloud subscription (or their local machine), which they have complete control of. Only the results of each pipeline run are sent back to the automated ML service, which then makes an intelligent, probabilistic choice of which pipelines should be tried next. In the workshop come learn how to use the service for a demand forecasting scenario and build a ready to deploy model.
Workshop material
- Powerpoint Deck - Download
- Workshop Intro Video : https://www.youtube.com/watch?v=xr5Z-QZ4V4s
- Workshop Notebook - https://aka.ms/globalainotebook
- Github samples - https://github.com/Azure/MachineLearningNotebooks/