Here’s how Red Hat is delivering on Enterprise AI
Red Hat is making a significant push into enterprise AI with a series of announcements aimed at integrating generative AI capabilities into its open-source offerings. The company’s strategy revolves around empowering its users to leverage AI in a more accessible and efficient manner, while also providing a robust platform for building and deploying AI-enabled applications.
One of the key highlights is the expansion of Red Hat Lightspeed, the company’s generative AI platform, to Red Hat OpenShift and Red Hat Enterprise Linux (RHEL). This integration will enable users to interact with these platforms using natural language, streamlining tasks such as application deployment, management, and maintenance. For instance, Lightspeed will provide recommendations for deploying new applications, autoscaling resources based on usage patterns, and flagging security vulnerabilities that require immediate attention.
Red Hat is also adding new features to its OpenShift AI platform, which is designed for building cloud-native AI-enabled applications. New features include model serving at the edge, allowing AI models to be extended to remote edge-based applications, and enhanced model serving capabilities for running both predictive and generative AI models on a single platform. Additionally, OpenShift AI will make the AI model development process easier with the addition of project workspaces and additional workbench images.
Another significant development is the introduction of “ automated policy as code” within the Red Hat Ansible Automation platform. This feature leverages AI algorithms to help enforce security and governance policies, maintaining compliance across hybrid cloud environments. Red Hat believes this is a crucial step in automation maturity, enabling organizations to better prepare their IT infrastructures for AI at scale.
Red Hat is also infusing generative AI capabilities into Konveyor, an open-source project for modernizing legacy applications by rebuilding them as cloud-native apps. Konveyor will now integrate with models like IBM watsonx Code Assistant, providing coding suggestions throughout the application modernization process, saving considerable time and effort.
Furthermore, Red Hat introduced Podman AI Lab, an extension to its Podman Desktop developer experience, which allows developers to build, test, and run generative AI-powered applications within containers on their personal computers and workstations. Podman AI Lab includes a recipe catalog with templates for common use cases, such as chatbots, text summarizers, code generators, and object detection.
Perhaps the most significant announcement is the introduction of RHEL AI, an enterprise-ready version of the InstructLab project and the Granite language and code models, built on top of RHEL.. It provides a foundation model platform for bringing open source-licensed GenAI models into the enterprise, including optimized bootable model runtime instances, supported and indemnified Granite models, and a supported distribution of InstructLab. This offering aims to simplify the deployment of AI models across hybrid infrastructures while leveraging Red Hat’s enterprise support and lifecycle promise.
Red Hat’s vision for AI extends beyond just running AI workloads; it encompasses model training and tuning across the open hybrid cloud, addressing limitations around data sovereignty, compliance, and operational integrity. The consistency delivered by Red Hat’s platforms across diverse environments is crucial for fueling AI innovation.
Overall, Red Hat’s announcements demonstrate a comprehensive approach to delivering enterprise AI, combining the power of open source with the robustness and support required for mission-critical AI deployments. By integrating generative AI capabilities into its core offerings and providing a robust platform for building and deploying AI-enabled applications, Red Hat is positioning itself as a major player in the enterprise AI landscape.
