Set the stage through a customer analytics use case and walk through a dashboard detailing a customer churn problem. In the labs, users interact with a web application before adding artificial intelligence (the “before” view) and then interact with the same web application after adding artificial intelligence (the “after” view.) Users will analyze the resulting cost and benefit that an analytics modernization project with a deployed machine learning model will bring to the business over time.
- Connect to various source repositories, including MongoDB and Db2
- Virtualize data: leave the data where it is and access and leverage it for analytic projects
- Add business categories and terms, and governance policies and rules
- Use machine learning to automatically map business terms to data assets, with quality scoring
- Shop for data in the enterprise catalog
- Transform multiple data sets into one view to be used for further analysis
- Build and interact with dashboards to gain insights into your data
- Create a machine learning model using a Jupyter notebook in Python
- Score and evaluate the machine learning model and make it available to an Application Developer
Deploy & Infuse
- Deploy the machine learning model and test it with APls or curl
- Infuse a web application with the machine learning model
- Provide the framework for Developers and Data Scientists to effectively collaborate
This event is targeted for Chief Data Officers, Data Scientists, Business Analysts, Data Engineers, Data Stewards, and anyone interested in improving their “time to value” by gaining insight from data and streamlining the process of automating machine learning model creation and deployment for AI infused applications.