Abstract: 

Artificial intelligence (AI)-powered applications are growing to deliver more mainstream experiences to users. Whether predicting rainfall and its effects on specific terrain or building a video game, AI models use machine learning (ML) to humanize the user experience in virtual or real world applications.

Operationalizing these applications with integrated ML capabilities and keeping them up to date—known as MLOps—ensures prediction accuracy. In this session, we will show you how to accelerate MLOps using an example of an object detection application.

MLOps requires collaboration amongst data scientists, developers, ML engineers, IT operations, and various DevOps technologies. This can require significant effort and coordination.

We’ll briefly discuss how data scientists build, test, and train ML models on Kubernetes hybrid cloud platforms such as Red Hat OpenShift. Next, we will explore how the integrated DevOps CI/CD capabilities in Red Hat OpenShiftⓇ (i.e., GitOps and Pipelines), allow us to automate and accelerate the integration of ML models into the application development process. Ultimately, these capabilities allow consistent, scaled application deployments, which also helps accelerate the frequent redeployment of updated ML models into production.

Session Outline

We’ll talk about:
• How Data Scientists build, test, and train ML models on Kubernetes hybrid cloud platforms like Red Hat OpenShift
• How Red Hat OpenShift incorporates GitOps and pipelines to automate and accelerate ML model integration during application development
• How the integrated DevOps CI/CD capabilities in Red Hat OpenShift facilitate on-demand application deployment and updates

Bio: 

Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat OpenShift Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.