Red Hat OpenShift / ODSC East 2021 | Open Data Science Conference

Business leaders desire data driven insights to help improve customer experience. Data engineers, data scientists, and software developers desire a self-service, cloud-like experience to access tools/frameworks, data, and compute resources anywhere to collaborate, build, and scale. This keynote will highlight AI/ML use cases, execution challenges, and tools to help accelerate AI/ML projects from pilot to production, and accelerate delivery of intelligent applications. Finally, the session will share real world success stories across a number of open source projects led by Red Hat, including Open Data Hub.

Know your customers—to keep an eye on them or to serve them better. Harnessing AI to streamline data harvesting for deeper insight-driven business decisions and rules helps organizations do both. See our demos that show both use cases based on the same hybrid cloud platform built completely from open source software.
One demo shows how to augment batch and real-time transaction processing with analytics and machine learning models derived from data and then how to integrate into investigations and suspicious activity reporting.

The second demo shows how to maintain a 360-degree view of the customer over various digital channels for communication, payments and any number of transactions by creating event-driven architectures for immediate response to customer situations. Embedding into these architectures AI and ML, and rules-based decision management can dramatically improve the customer experience.

The Ignite AI platform built by KPMG enables data management, model build/model management, solution development and deployment, and business enablement. Ignite is made for data scientists and engineers, but also allows for business user empowerment, keeping humans in the loop. The Ignite AI platform provides automated MLOps and data pipelines to achieve this goal.

The increased demand for transparent, explainable decision making, that is accurate, consistent and effective, has never been greater. Legislations like GDPR are just a result of increasing concerns about privacy, safety and transparency in general. While AI/ML solutions are great at making sense of high volumes of data, the reasoning process for most of the generated analytic models is usually quite opaque. Decision Management on the other hand, is a discipline that aims to provide full transparency on the decision process, but requires formalization of knowledge into decisions/rules, using some form of knowledge engineering (automated or not).

During this presentation, attendees will learn about a standards based, pragmatic approach to achieve the goals of eXplainable AI (XAI), combining decision models and analytic models. The approach promotes an effective method to increase transparency on automated decision making, without losing effectiveness.


In particular, presenters will demo how PMML (Predictive Modeling Markup Language), a well established standard for the representation of predictive models generated using Machine Learning can be transparently combined with DMN (Decision Model and Notation), a Decision Modeling standard that defines a high level language for decision automation. Attendees will have the opportunity to learn how the combination of these two Standards enhances and creates a high level effective solution for AI which can be explained and trusted.