Red Hat SummitDevelop, train, test, and deploy ML across the hybrid cloud
Red Hat® OpenShift® AI is an MLOps platform that allows you to develop, train, and deploy AI models and applications at scale across hybrid cloud environments.
Product overview
OpenShift AI offers organizations an efficient way to deploy an integrated set of common open source and 3rd-party tools to perform artificial intelligence and machine learning (AI/ML) modeling. Adopters gain a collaborative open source toolset and a platform for building experimental models without worrying about the infrastructure. They can then extend that base platform with partner tools to gain increased capabilities. Models can be served to production environments in a container-ready format, consistently, across cloud, on-premise, and edge environments.
OpenShift AI is a part of Red Hat AI. It provides IT operations with an environment that is simple to manage, with straightforward configurations on a proven, scalable, and security-focused platform.
Available as a traditional software product or managed cloud service, OpenShift AI supports popular generative AI (gen AI) foundation models, letting you fine tune and serve these pretrained models for your unique use cases and with your own data. You can even distribute workloads across multiple Red Hat OpenShift clusters, independent of their location. The platform is layered on top of OpenShift and makes it simpler to exploit AI hardware acceleration, supporting central processing unit (CPU) and graphic processing unit (GPU)-based hardware infrastructure, including NVIDIA and AMD GPUs and Intel XPUs—all without the need to stand up and manage your own data science platform.
Table 1. Features and benefits of Red Hat OpenShift AI
Highlights
Simplify the adoption of AI into your business, increase AI adoption, and provide flexibility in AI initiatives.
Establish AI/ML operational consistency across teams with a consistent user experience that empowers data scientists, data engineers, and DevOps teams to collaborate effectively.
Gain hybrid cloud flexibility with the ability to build, train, deploy, and monitor AI/ML workloads in the cloud, on-premise, or at the edge—close to where data is located.
Features | Benefits |
Model development tooling | Provides an interactive, collaborative interface based on JupyterLab for exploratory data science and model training, tuning, and serving. Data scientists have continuous access to core AI/ML libraries, widely-used frameworks, and an extensive array of predefined and customer-provided images and workbenches to accelerate model experimentation. |
Model training projects | Allow users to organize model development files, data connections and other artifacts needed for a given project, simplifying experimentation and enhancing collaboration. |
Model training distributed workloads | Use multiple cluster nodes simultaneously to more efficiently train and tune predictive and gen AI models, providing the scalability to handle tasks that might otherwise be computationally infeasible. |
GPUs and accelerators
| Provide self-service of GPU access by ITOps personnel to predefine their GPU resource environment, both on-premise and in the cloud, for their data scientists and application developers to simplify the selection of configurations for their project tasks. |
Data science pipelines | Allow data scientists and AI engineers to automate the steps to deliver and test models in development and production. Pipelines can be versioned, tracked and managed to reduce user error and simplify experimentation and production workflows. |
Model serving | Serves models from providers and frameworks like Hugging Face, ONNX, PyTorch, TensorFlow and others as well as popular serving runtimes like vLLM. Cluster resources, such as CPUs and GPUs, can be scaled across multiple nodes as workloads require. |
Model monitoring | Tracks metrics such as the number of successful and failed inference requests, average inference response times and compute uses to proactively adjust resources if needed. |
Drift detection | Monitors changes in input data distributions for deployed ML models to detect when the live data used for model inference significantly deviates from the data upon which the model was trained. |
Bias detection | Provides tooling to monitor whether their models are fair and unbiased, based on the training data but also for fairness during real-world deployments. |
Model registry | Offers a central place to view and manage registered models, helping data scientists share, version, deploy and track predictive and gen AI models, metadata and model artifacts. |
Disconnected environments and edge | Simplify the deployment of disconnected clusters on a restricted network, often behind a firewall, supporting industries where security and regulatory compliance require air-gapped deployments. |
In addition to the capabilities provided in OpenShift AI, many technology partner products have been integrated into the user interface (UI). These include Starburst for distributed data access across diverse data sets, Anaconda for package management, HPE for data lineage and versioning, NVIDIA for performance management of GPUs, Intel for high performance inference on Intel hardware, and Elastic for vector database with Retrieval Augmented Generation (RAG) applications.
Next steps:
Find out more about Red Hat OpenShift AI and watch the informative video.
