Daniel is a Senior Product Manager Azure Data at Microsoft. He is responsible for building the next generation of Big Data products for enterprise customers. Daniel focus on driving the features and experiences for Spark and in-database Machine Learning in the SQL Server Big Data Cluster product team. Daniel has a MsC in Big Data Analytics and over two decades career delivering data architecture and end to end solution across many verticals.
One of the biggest challenges which customers face is how to productionize machine learning for enterprises. Once the Data scientist, Data Engineers, Business analyst, Machine learning engineer have successfully built their Machine Learning Models, they need model management a system that manages and orchestrates the entire lifecycle of machine learning models. Analytical models must be trained, compared and monitored before deploying into production, requiring many steps to take place to operationalize a model's lifecycle. We have been looking at MLflow to be our open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. One of the features we have introduced into MLflow contributing back to the community is the ability to store models into backend SQL Server as the model artifact store.
The key focus here is that 'models are just like data' to an engine like SQL Server, and as such we can leverage most of the mission-critical features of data management built into SQL Server for machine learning models. Using SQL Server for ML model management, an organization can create an ecosystem for harvesting analytical models, enabling data scientists and business analysts to discover the best models and promote them for use. SQL Server treats models just like data â€“ storing them as serialized varbinary objects. SQL Server keeps the models 'close' to data, thus leveraging all the capabilities of a Management System for Data to be now nearly seamlessly transferable to machine learning models. This can help simplify the process of managing models tremendously resulting in faster delivery and more accurate business insights. We will also discuss how we are leveraging ONNX runtime in SQL and convert these models to ONNX and deploy the models on Edge for native predictions on the data.