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Red Hat OpenShift AI (RHOAI) Overview

RHOAI offers a versatile and scalable MLOps solution equipped with tools for rapid constructing, deploying, and overseeing AI-driven applications. Integrating the proven features of both Red Hat OpenShift AI and Red Hat OpenShift creates a comprehensive enterprise-grade artificial intelligence and machine learning (AI/ML) application platform, facilitating collaboration among data scientists, engineers, and app developers. This consolidated platform promotes consistency, security, and scalability, fostering seamless teamwork across disciplines and empowering teams to quickly explore, build, train, deploy, test machine learning models, and scale AI-enabled intelligent applications.

Formerly known as Red Hat OpenShift Data Science, OpenShift AI facilitates the complete journey of AI/ML experiments and models. OpenShift AI enables data acquisition and preparation, model training and fine-tuning, model serving and model monitoring, hardware acceleration, and distributed workloads using graphics processing unit (GPU) resources.

AI for All

Recent enhancements to Red Hat OpenShift AI include:

  • Implementation Deployment pipelines for monitoring AI/ML experiments and automating ML workflows accelerate the iteration process for data scientists and developers of intelligent applications. This integration facilitates swift iteration on machine learning projects and embeds automation into application deployment and updates.

  • Model serving now incorporates GPU assistance for inference tasks and custom model serving runtimes, enhancing inference performance and streamlining the deployment of foundational models.

  • With Model monitoring, organizations can oversee performance and operational metrics through a centralized dashboard, enhancing management capabilities.

Red Hat OpenShift AI ecosystem

Name Description
AI/ML modeling and visualization tools JupyterLab UI with prebuilt notebook images and common Python libraries and packages; TensorFlow; PyTorch, CUDA; and also support for custom notebook images
Data engineering Support for different Data Engineering third party tools (optional)
Data ingestion and storage Supports Amazon Simple Storage Service (S3) and NERC OpenStack Object Storage
GPU support Available NVIDIA GPU Devices (with GPU operator): NVIDIA A100-SXM4-40GB and V100-PCIE-32GB
Model serving and monitoring Model serving (KServe with user interface), model monitoring, OpenShift Source-to-Image (S2I), Red Hat OpenShift API Management (optional add-on), Intel Distribution of the OpenVINO toolkit
Data science pipelines Data science pipelines (Kubeflow Pipelines) chain together processes like data preparation, build models, and serve models