Enterprise grade Artificial Intelligence and Machine Learning (AI/ML) for Developers, Data Engineers, Data Scientists and Operations.
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Red Hat provides AI/ML across its products and platforms, giving developers a portfolio of enterprise-class AI/ML solutions to deploy AI-enabled applications in any environment, increase efficiency, and accelerate time-to-value. Red Hat draws upon key platforms and developer tools to achieve the following AI/ML objectives:
Red Hat platforms provide an end-to-end AI/ML solution from an underlying enterprise-grade operating system, to a Machine Learning operations (MLOps) platform, and finally to container-based orchestration and IT automation platforms. These platforms help you train, tune, and serve models used in purpose-build AI apps.
Red Hat OpenShift AI is Red Hat’s machine learning operations (MLOps) platform that includes: model development, monitoring and serving, AI lifecycle management, and hybrid cloud support. Red Hat OpenShift AI provides tools to rapidly develop, train, serve, and monitor machine learning models on site, in the public cloud, or at the edge.
Red Hat OpenShift is Red Hat’s open, hybrid cloud Kubernetes platform to build, run, and scale container-based applications - now with developer tools, CI/CD, and release management. Red Hat OpenShift extends DevOps to the entire ML lifecycle and simplifies deployment, scaling, and management of AI/ML training and serving.
Red Hat Enterprise Linux (RHEL) is a secure, stable, and supported enterprise-grade operating system with a robust ecosystem for rolling out new applications, virtualizing environments, integrating with other enterprise tools, and creating a secure hybrid cloud. RHEL provides support for core AI/ML libraries and hardware accelerators for the efficient processing of AI workloads.
Red Hat Ansible Automation Platform allows developers to set up IT automation to provision, deploy, and manage compute infrastructure across cloud, virtual, and physical environments. Red Hat Ansible Lightspeed with IBM watsonx Code Assistant is a generative AI service that helps automation teams learn, create, and maintain Ansible Automation Platform content more efficiently.
For the AI app developer, Red Hat has a suite of application development products and components, along with platforms for AI-enabled IT automation, developer productivity, and software supply chain management.
Red Hat’s suite of application development products includes Red Hat Runtimes & Languages, Red Hat Integration, Red Hat Developer Tools, and complementary platform components. These products enhance developer productivity through a self-service experience that abstracts away the technical details of application development.
Red Hat Ansible Lightspeed with IBM watsonx Code Assistant is a generative AI service available to Red Hat Ansible Automation Platform users that uses natural language processing to turn written prompts into code snippets for the creation of Ansible playbooks.
Red Hat Developer Hub is an enterprise-grade platform for building developer portals in a supported and opinionated framework. It is a unified, open, and AI-enabled platform designed to maximize developer skills, ease onboarding, and increase AI development productivity.
Red Hat Trusted Software Supply Chain brings Red Hat’s own open source software supply chain as a cloud service that enables AI developers to more quickly and efficiently code, build, and monitor their software using proven platforms, trusted content, and real-time security scanning and remediation.
Try these self-directed learning exercises to gain experience and bring your creativity to AI and Red Hat OpenShift AI – Red Hat’s dedicated platform for building AI-enabled applications. Learn about the full suite of MLOps to train, tune, and serve models for purpose-built applications.
Learn the foundations of Red Hat OpenShift AI, that gives data scientists and developers a powerful AI/ML platform for building AI-enabled applications. Data scientists and developers can collaborate to move quickly from experiment to production in a consistent environment.
Create a demo application using the full development suite: MobileNet V2 with Tensor input/output, transfer learning, live data collection, data preprocessing pipeline, and modeling training and deployment on a Red Hat OpenShift AI developer sandbox.
Learn engineering techniques for extracting live data from images and logs of the fictional bike-sharing app, Pedal. You will deploy a Jupyter Notebook environment on Red Hat OpenShift AI, develop a pipeline to process live image and log data, and also extract meaningful insights from the collected data.
Learn how a developer can work with RAG and LLM leveraging their own data...
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