In this session, we will demonstrate how easy the data scientists and developers can productise their AI/ML models in an cost-effective and agile mode using Open Source projects such as KServe, Codeflare or OpenDataHub, accelerating the AI/ML adoption using multiple open source libraries and frameworks among other AI/ML suites, without worrying about the infrastructure or lock-in from public-cloud specific tools.

We will explore how OpenDataHub can offer organizations a way to rapidly adopt MLOps and deploy an integrated set of common open source and third-party tools to perform AI/ML modeling all of that in a managed cloud service providing AI as a service.

Finally we will demonstrate how to train, deploy and operate an AI/ML model using the most famous libraries and frameworks.

In this session, we will demonstrate how to implement DevSecOps pipelines in production using Stackrox and Tekton and other Open Source Security tools such as Sigstore among others.

We will demonstrate how to eliminate security risks on our CICD pipelines implementing DevSecOps, and securing the software supply chain providing continuous scanning and runtime protection. On the other hand, we will demonstrate how to shift the security left, detecting and remediating vulnerabilities and misconfigurations that could affect the security of our workloads in production.

Finally we will depict how to provide to the developers automated guardrails, integrating Stackrox with DevOps and security tools such as Sigstore and Quay among others, building robust productive DevSecOps pipelines.