Open source-powered AI/ML for the hybrid cloud

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 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.