Machine Learning for the “Other 90%” of Business Use Cases
Webinar Speaker: James Kobielus, Senior Research Director, Data Management
Date: Tuesday, December 13, 2022
Time: 9:00 a.m. PT, 12:00 p.m. ET
Enterprises are seeking to infuse predictive models and other advanced analytics into a full range of business functions.
A key trend is providing business stakeholders with solutions for developing and deploying advanced analytics for various use cases. These next-generation solutions offer an interactive, visual, and no-code experience that enables business analysts to recognize patterns and drivers in historical data to create models that predict future outcomes. These machine learning (ML) models are at the heart of today’s most powerful predictive analytics applications.
In this webinar, TDWI senior research director James Kobielus will discuss how a new generation of augmented analytics solutions deliver the value of ML-driven predictive intelligence throughout their operations and how:
- Business analysts can build and deploy ML-driven predictive applications
- Automated machine learning generates real-time insights for nontraditional developers, producing highly predictive models
- ML benefits a variety of predictive analytics needs including sales forecasting, churn reduction, customer lifetime value, inventory optimization, and more
He will be joined by Chris Mabardy, Qlik senior director of product marketing, who will share how automation and augmentation of machine learning is delivering significant value for Qlik customers.
Guest Speaker
Sean Stauth
Director – AutoML Solutions
Qlik
As Director of AutoML Solutions, Sean helps Qlik’s global customer base gain develop and gain success with their AI and Machine Learning initiatives. A believer in the power of AI and predictive analytics to help companies with their strategic need, Sean has spent his career helping companies build AI and data driven products. Sean has held product development, consulting, and product management roles at Accenture, Apple, Silicon Valley Data Science, and most recently Big Squid.
James Kobielus