Democratizing Machine Learning on the SQL Platform

Would you like to build end to end machine learning scenarios forecasting demand for your customers and become the next AI driven startup focused on intelligent cloud and intelligent edge, leveraging the data stored inside your most trusted, most secure, hyperscale Azure SQL Databases for Managed Instance in the cloud or your on-premises SQL Server in your own data center? Come and join us this session to unleash the power from your data as we introduce Machine Learning Services for Python and R across the SQL Platform. In this session we will walk through how SQL Server Machine Learning Services lets you execute Python and R scripts in-database. You can use it to prepare and clean data, do feature engineering, and train, evaluate, and deploy machine learning models within a database. The feature where the data resides and eliminates transfer of the data across the network to another server. You can install and use open-source packages and frameworks, such as PyTorch, TensorFlow, and scikit-learn to build the machine learning and deep learning models you have been planning to build for so long. You can also deploy existing models to Azure SQL Database for Managed Instance, Edge or SQL Server Big Data Cluster and use relational data for making predictions.