Moving an AI project from a local laptop to a massive cloud cluster often feels like trying to fit a square peg in a round hole. Fragmented infrastructure and brittle code frequently cause pilot projects to stall before reaching production. To solve this, AWS has partnered with Union.ai to bring Flyte—an open-source orchestration system—to Amazon EKS, allowing developers to manage complex machine learning pipelines using familiar Python code instead of complex infrastructure languages.

The system introduces a compute-aware approach where the infrastructure automatically provisions the exact resources needed for each task, whether it is a simple data shuffle or a massive GPU-heavy training session. By using a managed control plane, teams can maintain complete ownership of their data within their own AWS account while benefiting from enterprise-grade reliability. This setup effectively bridges the gap between experimentation and deployment by ensuring every execution is versioned and cached.

Beyond simple model training, the platform is now optimized for the next wave of agentic AI—long-running systems that make autonomous decisions. With the inclusion of Amazon S3 Vectors, developers can build sophisticated Retrieval Augmented Generation (RAG) workflows that allow models to access and remember vast amounts of custom data securely. This enables a more resilient environment where workflows can automatically recover from crashes without human intervention, significantly cutting down on development cycles.

Moving an AI project from a local laptop to a massive cloud cluster often feels like trying to fit a square peg in a round hole. Fragmented infrastructure and brittle code frequently cause pilot projects to stall before reaching production. To solve this, AWS has partnered with Union.ai to bring Flyte—an open-source orchestration system—to Amazon EKS, allowing developers to manage complex machine learning pipelines using familiar Python code instead of complex infrastructure languages.

The system introduces a compute-aware approach where the infrastructure automatically provisions the exact resources needed for each task, whether it is a simple data shuffle or a massive GPU-heavy training session. By using a managed control plane, teams can maintain complete ownership of their data within their own AWS account while benefiting from enterprise-grade reliability. This setup effectively bridges the gap between experimentation and deployment by ensuring every execution is versioned and cached.

Beyond simple model training, the platform is now optimized for the next wave of agentic AI—long-running systems that make autonomous decisions. With the inclusion of Amazon S3 Vectors, developers can build sophisticated Retrieval Augmented Generation (RAG) workflows that allow models to access and remember vast amounts of custom data securely. This enables a more resilient environment where workflows can automatically recover from crashes without human intervention, significantly cutting down on development cycles.