Open source-powered AI/ML for the hybrid cloud
Enterprise grade Artificial Intelligence and Machine Learning (AI/ML) for Developers, Data Engineers, Data Scientists and Operations.
Overview
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 requires platforms with capabilities for both machine learning operations (MLOps) and application development.
Hybrid cloud AI/ML platforms combine MLOps and application platform capabilities by:
- Providing developers, data engineers, data scientists, and operations teams with consistency in how applications and models are developed, packaged, deployed, and managed.
- Developing, training, tuning, deploying, and serving models and applications as containerized workloads through common interfaces and tools without dealing directly with the underlying complexities of a Kubernetes configuration, orchestration, security, and compliance with mature cloud native CI/CD practices.
- Supporting containerized AI workloads and their specialized needs.
- Enabling an ecosystem of specialized best-in-class open source projects and ISV software that complement and extend the platform.
Hybrid cloud AI/ML platform capabilities
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.
AI/ML learning exercises
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.
Red Hat platforms
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-built AI apps.
Latest Artificial Intelligence articles
Learn how vLLM outperforms Ollama in high-performance production deployments,...
Learn how to perform large-scale, distributed batch inference on Red Hat...
Applied AI for Enterprise Java Development
For Java enterprise developers and architects looking to expand their skill set into artificial intelligence and machine learning (AI/ML), getting started can feel intimidating, especially when faced with complex theory, data science, and unfamiliar programming languages.
Download Applied AI for Enterprise Java Development, a book about AI tailored specifically for Java developers. This practical guide shows you how to integrate generative AI, large language models, and machine learning into your existing Java enterprise ecosystem, using tools and frameworks you already know and love. By combining the reliability of Java’s enterprise framework with the power of AI, you’ll unlock new capabilities to elevate your development process and deliver innovative solutions.
- Get clear explanations of key AI techniques, hands-on examples, and real-world projects to help you build AI-powered applications without abandoning the familiar Java environment.
- Explore foundational AI concepts and learn how to apply core technologies to transform your Java projects into cutting-edge, modern applications.
