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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 withcapabilities for both machine learning operations (MLOps) and application development.

MLOps platform

An MLOps platform, with workflows inspired by DevOps and GitOps principles, to integrate ML models into the software development process. 

• A flexible and scalable platform with tools to build, deploy, and manage AI-enabled applications.
• Leverage the vast number of pre-trained models from open source providers. 
• Utilize ML frameworks and serving formats like Pytorch, Tensorflow, ONNX, and others for model development.
• Deliver inference in a hybrid cloud environment at high performance and throughput.

Application platform

A consistent Kubernetes-based application platform for development, deployment, and management of existing and modernized cloud-native applications that runs on any cloud. 

• A wide range of languages, runtimes, and frameworks to develop business logic. 
• Integration technologies like API Management, SSO and more, that allow the applications to be exposed securely and at scale.
• Tools that support modern CI/CD DevOps practices.
• Developer Tools that enable seamless on-boarding and DevSecOps.

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.