人工智能+工具方案目录

Red Hat OpenShift AI is a comprehensive MLOps platform for building, deploying, and managing artificial intelligence (AI) and machine learning (ML) models at scale across the hybrid cloud. It provides a unified environment with a curated set of tools to streamline the entire machine learning and AI lifecycle, from data preparation and model training to production deployment and monitoring. By fostering collaboration between data scientists, software developers, and IT operations, it accelerates the delivery of AI-powered applications.

Solution Capabilities: 1. Enterprise AI platform Foundation: Built on Red Hat OpenShift, it provides a secure, scalable, and robust platform for both developing models and hosting the final AI applications. This ensures efficient GPU resource management and reliable operations across on-premises and hybrid cloud environments. 2. Intelligent Model Serving: Acts as the essential inference engine that connects models to applications. It offers high-performance, scalable serving for a broad range of models, including a wide array of LLMs, enabling you to bring generative AI applications to life. 3. End-to-End MLOps Lifecycle: Manages the entire AI/ML workflow in one place. It combines collaborative model development tools with automated pipelines for MLOps, a central registry for governance, and integrated monitoring to ensure models remain accurate and fair. 4. Powered by Open Source Innovation: Gives you the flexibility to use the best open-source tools and models without vendor lock-in. It allows you to leverage rapid community-driven advancements in AI while maintaining enterprise-grade stability and support.

OpenShift AI is a versatile platform applicable to a wide range of machine learning use cases: 1. Accelerating AI Adoption: Streamline the path from model experimentation to production by automating MLOps workflows, enabling faster AI application development and deployment of AI features. 2. Generative AI & Large Language Models (LLMs): Build and host powerful generative AI applications, such as Retrieval-Augmented Generation (RAG) systems that use company data to provide context-aware, accurate responses. 3. Powering 3rd Party Agentic AI Frameworks: Serve as the underlying container and model serving platform for advanced applications built with external agentic AI frameworks (e.g., LangChain, DIFY). You bring the agent framework, and OpenShift AI provides the robust environment to run the agents, their tools, and the LLMs they depend on. 4. Model Lifecycle Management: Implement robust governance with tools for model versioning, serving, and monitoring to ensure deployed models remain accurate, fair, and performant over time.