Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization

The global market opportunity for machine learning applications is rapidly increasing ~40% year-over-year.

One of the main prerequisites for successful ML- and analytics projects is access to the right data.

Another important prerequisite is effective collaboration, not only between developers, but across the whole enterprise.

We explain how an Enterprise Insights Platform can be used by developers to quickly enhance a Stock Trading Application.

The platform enables developers to quick find and access the required data, to deploy integrated data (vs. data integration on application level) and helps to extend applications with machine learning capabilities.In the demo part of the session we show a hands-on demo of  IBM Cloud Pak for Data. It is based on Kubernetes and many other Open Source projects. The platform offers a high degree of automation in areas like data quality analysis and enrichment, data integration, selection of machine learning algorithms and model deployment into production. We explain how developers can deploy various services like data virtualization, the data catalog, and the Auto AI experiment for simplified data access, data exploration and simplified ML model selection.