Abstract: The accessibility of enterprise AI solutions, combined with mobility of data managed in the public cloud, dramatically lowers the barriers of applying data science tools to business intelligence programs. This democratization of data science enables predictive and prescriptive analysis to be incorporated directly into business processes and lets organizations make smarter data-driven decisions. Harnessing the potential of this AI/BI convergence can be facilitated by a business semantic layer that simplifies data models and exposes a consistent set of features to AI engines.
Topics We Will Cover
1. Practical considerations for incorporating AI/ML technologies into BI programs to improve the capability to automate insights and augment decision making with prescriptive and predictive analyses.
2. Relating architecture of modern enterprise AI solutions to cloud data platforms
3. The power of a business semantic layer to simplify integration between AI engines and cloud data
4. Creating flexible consumption options for AI/ML augmented insights - BI platforms, Excel, Jupyter Notebooks
Bio: Daniel Gray brings rich experience in technical solutions engineering as well as software engineering to his work with global enterprise organizations. Prior to joining AtScale to lead the Solutions Engineering team, Daniel spent many years in the analytics space including Hewlett-Packard's Advanced Technology Center, Vertica, and Domino Data Lab. When he's not in the office or onsite with customers, you'll find Daniel running, climbing, hiking, and biking - basically anything outdoors.