Red Hat has been instrumental in driving many open source community efforts around artificial intelligence and machine learning (AI/ML) technologies for more than 6 years through the Open Data Hub project. Open Data Hub is a fully open project that supports end-to-end AI/ML workflows running in containers on Red Hat® OpenShift®, a Kubernetes-powered application platform. Many organizations have used some of the technologies curated in the Open Data Hub project—such as Jupyter, TensorFlow, Pytorch, Kubeflow, and Ray—to jumpstart their own do-it-yourself efforts aimed at creating a core data science platform to build intelligent applications. In addition to working closely with the open source community, Red Hat has responded to numerous requests to offer a paid and supported version of Open Data Hub. This has resulted in Red Hat OpenShift Data Science, a powerful platform for building, deploying, and monitoring AI/ML models and applications across on-premise, public cloud, and edge environments. Red Hat continues to invest in Open Data Hub and refine the upstream technologies it continues to curate on Red Hat OpenShift, while building out experimentation and MLOps capabilities that feed into OpenShift Data Science. In this episode, we’ll take you through how to get the most out of open source AI, introduce you to OpenShift Data Science, and provide a roadmap for planned future investments by Red Hat for AI on Red Hat OpenShift.
Steven Huels has more than 20 years of experience with data management and analytic platforms as a consultant, developer, product owner, engineering manager, and business unit owner. He is Senior Director for Red Hat's AI platform business, focusing on Red Hat's artificial intelligence strategy, ecosystem partners, and customer implementations. Steven is one of the founders of the Open Data Hub project—a reference architecture for building a data science platform on Red Hat OpenShift—and enjoys speaking with customers who are exploring how to build out their AI/ML platforms.
Sherard Griffin has been at Red Hat since 2017 and is currently Senior Director of AI Services in Red Hat Engineering. His responsibilities include the development of Red Hat OpenShift Data Science, a new managed service for data scientists to quickly develop, train, and test models in cloud environments. Sherard has spent his time at Red Hat advocating for ways customers can democratize access to scalable hybrid cloud AI technologies and platforms within their organizations. He’s also responsible for Open Data Hub, a community-driven reference architecture for building an Artificial Intelligence-as-a-Service (AIaaS) platform on Red Hat OpenShift and AI services running in Red Hat’s datacenters, where large amounts of data are processed, stored, and made available daily to analysts, developers, and data scientists across the company.
David Williams manages the Data Science and Edge Practice at Red Hat. Its mission is to help customers deploy data science platforms, machine learning applications, and edge workloads with Red Hat’s technologies. His career has centered on model-based development techniques that span optimization, decision and process models, and data product models. He has served as the product manager for IBM Operational Decision Manager, a leading decision management product; a government-sponsored video game; and a complexity science-inspired optimization engine. He has led multinational consulting efforts in Japan, Mexico, and the United States. David now specializes in consulting services delivery and management and innovating with Red Hat’s open source technologies.
