Intelligent experiences powered by AI can seem like magic to users. Developing them, however, is pretty cumbersome involving a series of sequential and interconnected decisions along the way that is 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. Similar to 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.
By making Automated ML available through the Azure Machine Learning service (Python-based SDK), we're empowering data scientists with a powerful productivity tool. We also have Automated ML available through PowerBI so that business analysts and BI professionals can also take advantage of machine learning. For developers familiar with Visual Studio and C#, we now have Automated ML available in C#.Net. If you are a SQL data engineer, we have a solution for you as well. And stay tuned as we continue to incorporate it into other product channels to bring the power of Automated ML to everyone!
This session will provide an overview of Automated machine learning, how it works and how you can get started! We will walk through real-world use cases, build ML models using Automated ML and go through the E2E ML process of training, deployment, inferencing and operationalization of models.