This week’s Red Hat Summit saw many of the company’s broad AI aspirations announced last year come to fruition. The high-level theme, introduced in Tuesday’s keynote address by President and CEO Matt Hicks, centered on how Red Hat has doubled down on AI throughout its portfolio and in its efforts with industry partners.
Red Hat’s origins and its open-source focus over the years was reflected in the news from the conference. Specifically, Hicks noted that, as a company, Red Hat believes open source is the best model for AI and AI innovations.
A Platform for AI
Red Hat announced that it provides the AI platforms that organizations need to connect their clouds, data, workloads, and IT infrastructure by infusing AI capabilities into its open-source platforms. For instance, Red Hat introduced new cross-portfolio Lightspeed offerings, updates to Red Hat OpenShift AI, and more to bring automated AI capabilities into its hybrid cloud technologies.
Some of the key announcements in this AI as a platform area included:
Red Hat Enterprise Linux AI (RHEL AI) brings together the open source-licensed Granite large language model (LLM) family from IBM Research, InstructLab model alignment tools based on the LAB (Large-scale Alignment for chatBots) methodology, and a community-driven approach to model development through the InstructLab project.
The solution is packaged as an optimized, bootable RHEL image for deployments across hybrid cloud infrastructure. The solution is also included as part of OpenShift AI, Red Hat’s hybrid machine learning operations (MLOps) platform, for running models and InstructLab at scale across distributed cluster environments.
The expansion of Red Hat Lightspeed across its platforms, “infusing enterprise-ready AI across the Red Hat hybrid cloud portfolio,” according to the company. In this week’s news, Red Hat OpenShift Lightspeed and Red Hat Enterprise Linux Lightspeed will now offer natural language processing capabilities designed to make Red Hat’s enterprise-grade Linux and cloud-native application platforms easier to use for users of any skill level. It does this through an integration with generative AI technology.
Automated policy as code is a new capability coming in future versions of the Red Hat Ansible Automation Platform. The capability will help enforce policies and compliance across hybrid cloud environments that increasingly include a varied and growing number of AI applications.
Podman AI Lab, an extension for Podman Desktop, that gives developers the ability to build, test, and run generative artificial intelligence (GenAI)-powered applications in containers using an intuitive, graphical interface on their local workstation.
See also: Red Hat Simplifies GenAI Development and AI Search
Taking Open Source into New Application Areas
A prime benefit of providing an open-source platform is that it can spawn innovation in numerous application areas. A series of partner announcements from the conference offered a glimpse into what is possible.
One of the most interesting efforts from the conference was the announcement of an expanded collaboration with Deloitte to help manage the complexity of multi-vendor software ecosystems across industries. One specific area the two companies will concentrate on is a comprehensive solution to support the development, systems engineering, testing, and operations of software-defined vehicle (SDV) product lines. With this collaboration, Deloitte and Red Hat will help organizations accelerate the transition from traditional hardware development methodologies to software-defined development cycles.
SDVs are essentially edge data centers that deliver new capabilities and functionality to vehicles. To address the needs of this market, Red Hat and Deloitte have designed a pre-integrated, multi-vendor solution for SDVs based on the Red Hat In-Vehicle Operating System.
The pre-integrated solution also includes Red Hat OpenShift, serving as the application platform offering a DevOps environment with a strong security posture and Red Hat Ansible Automation Platform and Red Hat Quay for greater mission-critical automation and container image security capabilities in SDV development workflows.
Those solutions are complemented by Deloitte’s SDV portfolio, which includes services and capabilities for systems engineering, DevSecOps, validation and verification, virtualization, and modern engineering. Deloitte’s capabilities are supported by generative AI (GenAI) for quality assurance and development teams, including use cases for spec-to-code generation, test case coverage, and intelligent vehicle diagnostics.
A Final Word
AI and automation are top priorities for most organizations today. However, many organizations face great challenges when they try to implement both technologies into their existing infrastructures and workflows.
Open-source AI and automation solutions help ease the problems. They come with an eager community of developers and evangelists that can bring attention to new issues and get them resolved quickly.
Announcements from the conference put teeth into Red Hat’s approach to AI and automation. Similar to what was done with cloud and open source, Red Hat is now doing the same for AI with a platform for AI approach.
Salvatore Salamone is a physicist by training who has been writing about science and information technology for more than 30 years. During that time, he has been a senior or executive editor at many industry-leading publications including High Technology, Network World, Byte Magazine, Data Communications, LAN Times, InternetWeek, Bio-IT World, and Lightwave, The Journal of Fiber Optics. He also is the author of three business technology books.
