Build AI workflows on Amazon EKS with Union.ai and Flyte

2026-02-19 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Advanced, long

Summary

Union.ai 2.0 and Flyte offer robust solutions for orchestrating and scaling AI/ML workflows on Amazon Elastic Kubernetes Service (Amazon EKS), addressing common challenges like infrastructure complexity, reproducibility, and cost management. Union.ai 2.0, built on the open-source Flyte, provides enterprise-grade managed services, enhancing scalability, reliability, and compliance for teams running AI/ML workloads on Amazon EKS. It integrates seamlessly with AWS services such as Amazon S3, Amazon Aurora, AWS IAM, and Amazon CloudWatch. The platform supports pure Python workflows, dynamic execution, and compute-aware orchestration, reducing code by 66% compared to traditional orchestrators. A key new feature is the integration with Amazon S3 Vectors, enabling cost-optimized vector storage for Retrieval Augmented Generation (RAG), semantic search, and agentic AI workflows, as demonstrated by a multi-agent trading simulation example. Deployment options range from fully managed Union BYOC to self-managed Union and open-source Flyte on Amazon EKS via AWS CDK.

Key takeaway

For AI Engineers and MLOps teams struggling to move AI projects from pilot to production on Kubernetes, consider adopting Union.ai 2.0 with Flyte on Amazon EKS. This approach provides managed infrastructure, enhanced scalability, and built-in reproducibility, significantly reducing operational overhead and accelerating iteration cycles. You can leverage its Amazon S3 Vectors integration to simplify vector data management for RAG and agentic AI applications, ensuring compliance and cost efficiency.

Key insights

Union.ai 2.0 and Flyte streamline AI/ML workflow orchestration on Amazon EKS, enhancing scalability, reliability, and vector data management.

Principles

• Reproducibility is default for every execution.
• Orchestration logic should be pure Python.
• Dynamic resource provisioning optimizes compute.

Method

Orchestrate AI/ML workflows using Flyte's Python SDK on Amazon EKS, leveraging Union.ai 2.0 for managed operations, dynamic execution, and Amazon S3 Vectors integration.

In practice

• Use Amazon S3 Vectors for RAG and semantic search.
• Deploy Union.ai 2.0 for managed EKS AI/ML operations.
• Implement multi-agent systems with stateful orchestration.

Topics

• AI/ML Workflow Orchestration
• Amazon EKS
• Flyte Union.ai
• Amazon S3 Vectors
• MLOps

Code references

• awslabs/cdk-eks-blueprints
• unionai-oss/deploy-flyte
• unionai/unionai-examples
• awslabs/ai-on-eks

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• How to Orchestrate AI Workflows — Pure AI workflows are fast to prototype but lack reliability; combine AI with deterministic orchestration for robust systems.
• OpenAI and Amazon announce strategic partnership — OpenAI and Amazon partner to integrate advanced AI models and infrastructure, expanding enterprise AI capabilities and compute capacity.
• AWS WorkSpaces Now Lets AI Agents Operate Legacy Desktop Applications without APIs — AWS WorkSpaces now enables AI agents to operate legacy desktop applications without APIs, using computer vision and input simulation.
• AWS enters the context layer race with a graph that learns from agents, not manual curation — AWS's new context layer automatically builds and refines knowledge graphs through agent interaction, reducing manual curation.
• 9 ways AI is reshaping enterprise operations: Key insights from AWS Summit NYC — Enterprise AI is rapidly moving from experimentation to production, driven by agentic systems and physical AI.
• Kubernetes in the Age of AI — Kubernetes is the unified orchestration layer for both traditional and compute-intensive AI workloads, including generative and agentic AI.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.