# Orchestrating Production AI: Union.ai and Flyte on Amazon EKS

> Coverage of aws-ml-blog

**Published:** February 19, 2026
**Author:** PSEEDR Editorial
**Category:** stack

**Tags:** MLOps, Kubernetes, Amazon EKS, Flyte, Union.ai, AI Orchestration

**Canonical URL:** https://pseedr.com/stack/orchestrating-production-ai-unionai-and-flyte-on-amazon-eks

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In a recent technical guide, the aws-ml-blog outlines a reference architecture for building and scaling AI workflows using Union.ai and Flyte on Amazon Elastic Kubernetes Service (EKS).

The "pilot to production" gap remains a persistent bottleneck in the machine learning lifecycle. While data scientists excel at building models in isolated environments, translating those experiments into resilient, scalable pipelines often reveals a chasm of infrastructure complexity. The challenge is rarely the model itself, but rather the fragmented processes and brittle tooling used to deploy it. Generic orchestrators often lack the data awareness required for ML, while custom scripts lack scalability.

This post addresses that friction by introducing a stack centered on Flyte, an open-source workflow orchestrator designed specifically for data and ML workloads. While Kubernetes provides the necessary compute elasticity for these tasks, managing the orchestration layer on top of it can be daunting. The article explains how Union.ai acts as a managed control plane, deploying Flyte on Amazon EKS to abstract away the underlying infrastructure hurdles. This setup allows teams to utilize the scalability of EKS without requiring data scientists to master Kubernetes manifests.

The authors demonstrate how the Flyte Python SDK allows developers to define workflows programmatically, ensuring strong typing and reproducibility. Furthermore, the piece details the integration of this orchestration layer with essential AWS primitives-using Amazon S3 for object storage, Amazon Aurora for persistent metadata, and AWS IAM for granular security controls. By leveraging Union.ai 2.0, teams can bypass the heavy operational lift of managing the cluster configuration, focusing instead on the logic of the AI application. The post concludes with a practical example involving vector data, illustrating how these components coalesce into a functioning production pipeline.

For engineering teams struggling with the operational overhead of Kubernetes-based ML pipelines, this guide offers a practical architecture for regaining control.

[Read the full post](https://aws.amazon.com/blogs/machine-learning/build-ai-workflows-on-amazon-eks-with-union-ai-and-flyte)

### Key Takeaways

*   \*\*Infrastructure as a Barrier\*\*: The primary failure mode for AI projects transitioning to production is fragmented infrastructure, not model quality.
*   \*\*Managed Orchestration\*\*: Union.ai leverages Amazon EKS to deploy Flyte, abstracting Kubernetes complexity from the data science workflow.
*   \*\*Native AWS Integration\*\*: The solution hooks directly into Amazon S3, Aurora, and IAM, creating a secure and persistent environment for ML metadata and artifacts.
*   \*\*Reproducibility via SDK\*\*: The Flyte Python SDK enables teams to define strongly typed, reproducible workflows that scale automatically on EKS.

[Read the original post at aws-ml-blog](https://aws.amazon.com/blogs/machine-learning/build-ai-workflows-on-amazon-eks-with-union-ai-and-flyte)

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## Sources

- https://aws.amazon.com/blogs/machine-learning/build-ai-workflows-on-amazon-eks-with-union-ai-and-flyte