Covering Disruptive Technology Powering Business in The Digital Age

Red Hat, Inc., the world’s leading provider of open source solutions, announced Podman AI Lab, an extension for Podman Desktop, at Red Hat Summit 2024, along with Red Hat Enterprise Linux AI (RHEL AI).

Podman AI Lab 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. This contributes to the democratisation of GenAI, and gives developers the benefits of convenience, simplicity, and cost efficiency of their local developer experience while maintaining ownership and control over sensitive data.

RHEL AI, on the other hand, serves as a foundation model platform that enables users to more seamlessly develop, test, and deploy GenAI models, driving even more the open sourcing of AI.

The recent surge of GenAI and open source large language models (LLMs) has ushered in a new era of computing that relies heavily on the use of AI-enabled applications, and organizations are moving quickly to establish expertise, processes, and tools to remain relevant. Industry analyst firm IDC notes this shift, predicting “By 2026, 40% of net-new applications will be intelligent apps, where developers incorporate AI to enhance existing experiences and form new use cases.”

Podman AI Lab to Fuel Developer Adoption of GenAI

As AI and data science move into mainstream application development, tools like Podman AI Lab can help fuel developer adoption of GenAI for building intelligent applications or enhancing their workflow using AI-augmented development capabilities. The Podman AI Lab features a recipe catalogue with sample applications that give developers a jump start on some of the more common use cases for LLMs, including:

• Chatbots that simulate human conversation, using AI to comprehend user inquiries and offer suitable responses. These capabilities are often used to augment applications that provide self-service customer support or virtual personal assistance.
• Text summarisers, which provide versatile capabilities across many applications and industries, where they can deliver effective and efficient information management. Using this recipe, developers using Podman AI Lab can build applications to assist with things like content creation and curation, research, news aggregation, social media monitoring, and language learning.
• Code generators, which empower developers to concentrate on higher-level design and problem-solving by automating repetitive tasks like project setup and API integration, or to produce code templates.
• Object detection helps identify and locate objects within digital images or video frames. It is a fundamental component in various applications, including autonomous vehicles, retail inventory management, precision agriculture, and sports broadcasting.
• Audio-to-text transcription involves the process of automatically transcribing spoken language into written text, facilitating documentation, accessibility, and analysis of audio content.

These examples within Podman AI Lab provide an entry point for developers where they can review the source code to see how the application is built and learn best practices for integrating their code with an AI model.

For developers, containers have traditionally provided a flexible, efficient, and consistent environment for building and testing applications on their desktops without worrying about conflicts or compatibility issues. Today, they are looking for the same simplicity and ease of use for AI models. Podman AI Lab helps meet this need by giving them the ability to provision local inference servers, making it easier to run a model locally, get an endpoint, and start writing code to wrap new capabilities around the model.

In addition, Podman AI Lab includes a playground environment that allows users to interact with models and observe their behavior. This can be used to test, experiment, and develop prototypes and applications with the models. An intuitive user prompt helps in exploring the capabilities and accuracy of various models and aids in finding the best model and the best settings for the use case in the application.

Democratising AI with RHEL AI

Aside from introducing Podman AI Lab, Red Hat also announced RHEL AI, which brings together the open source-licenced 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 entire solution is packaged as an optimised, bootable RHEL image for individual server deployments across the hybrid cloud and 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 launch of ChatGPT generated tremendous interest in GenAI, with the pace of innovation only accelerating since then. Enterprises have begun moving from early evaluations of GenAI services to building out AI-enabled applications. A rapidly growing ecosystem of open model options has spurred further AI innovation and illustrated that there won’t be “one model to rule them all.” Customers will benefit from an array of choices to address specific requirements, all of which stands to be further accelerated by an open approach to innovation.

Implementing an AI strategy requires more than simply selecting a model; technology organisations need the expertise to tune a given model for their specific use case, as well as deal with the significant costs of AI implementation. The scarcity of data science skills are compounded by substantial financial requirements including:

• Procuring AI infrastructure or consuming AI services
• The complex process of tuning AI models for specific business needs
• Integrating AI into enterprise applications
• Managing both the application and model lifecycle.

To truly lower the entry barriers for AI innovation, enterprises need to be able to expand the roster of who can work on AI initiatives while simultaneously getting these costs under control. With InstructLab alignment tools, Granite models, and RHEL AI (along with Podman AI Lab), Red Hat aims to apply the benefits of true open source projects—freely accessible and reusable, transparent and open to contributions—to GenAI in an effort to remove these obstacles.

Open Source AI Innovation on a Trusted Linux Backbone

RHEL AI builds on this open approach to AI innovation, incorporating an enterprise-ready version of the InstructLab project and the Granite language and code models along with the world’s leading enterprise Linux platform to simplify deployment across a hybrid infrastructure environment. This creates a foundation model platform for bringing open source-licenced GenAI models into the enterprise. RHEL AI includes:

• Open source-licenced Granite language and code models that are supported and indemnified by Red Hat.
• A supported, lifecycled distribution of InstructLab that provides a scalable, cost-effective solution for enhancing LLM capabilities and making knowledge and skills contributions accessible to a much wider range of users.
• Optimized bootable model runtime instances with Granite models and InstructLab tooling packages as bootable RHEL images via RHEL image mode, including optimized Pytorch runtime libraries and accelerators for AMD Instinct™ MI300X, Intel and NVIDIA GPUs and NeMo frameworks.
• Red Hat’s complete enterprise support and lifecycle promise that starts with a trusted enterprise product distribution, 24×7 production support, and extended lifecycle support. 

As organisations experiment and tune new AI models on RHEL AI, they have a ready on-ramp for scaling these workflows with Red Hat OpenShift AI, which will include RHEL AI, and where they can leverage OpenShift’s Kubernetes engine to train and serve AI models at scale and OpenShift AI’s integrated MLOps capabilities to manage the model lifecycle.  IBM’s watsonx.ai enterprise studio, which is built on Red Hat OpenShift AI today, will benefit from the inclusion of RHEL AI in OpenShift AI upon availability, bringing additional capabilities for enterprise AI development, data management, model governance, and improved price performance.

RHEL AI is now available as a developer preview. Building on the GPU infrastructure available on IBM Cloud, which is used to train the Granite models and support InstructLab, IBM Cloud will now be adding support for RHEL AI and OpenShift AI. This integration will allow enterprises to deploy generative AI more easily into their mission-critical applications.

(0)(0)