Enterprise grade Artificial Intelligence and Machine Learning (AI/ML) for Developers, Data Engineers, Data Scientists and Operations.
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Open source software is at the heart of cutting edge innovation like Generative AI in addition to its already prominent role in powering Predictive AI. To deliver these innovations at a global scale, enterprises have to deal with the complexities of security, privacy, compliance, reliability, scale, and performance. To handle these complexities, enterprises usually end up with a hybrid cloud footprint where their data and applications are deployed on environments ranging from on-prem data centers to hyperscaler cloud provider infrastructure. Operationalizing AI/ML and utilizing open source powered AI/ML in intelligent applications that deliver exponentially enhanced customer experiences in a hybrid cloud environment require platforms with capabilities for both machine learning operations (MLOps) and application development.
An MLOps platform, with workflows inspired by DevOps and GitOps principles, to integrate ML models into the software development process.
A consistent Kubernetes-based application platform for development, deployment, and management of existing and modernized cloud-native applications that runs on any cloud.
Learn about the capabilities of a hybrid cloud AI/ML platform including AI workloads, an integrated MLOps and application development platform, and developer productivity tools.
Containerized workloads deployed across the hybrid cloud, based on the core AI techniques of machine learning (ML) and deep learning where data and information drive these workloads.
A common platform to bring IT, data science, and app dev teams to support the end-to-end lifecycle of ML models and cloud native applications.
AI-enabled code generation, internal developer portals, and MLSec Ops that enhance the developer experience through open source powered developer tools.
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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Join us if you’re a developer, software engineer, web designer, front-end designer, UX designer, computer scientist, architect, tester, product manager, project manager or team lead.