# Amazon Machine Learning

A legacy AWS service for training and evaluating machine learning models and generating predictions.

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## Building a Minimal Control Plane to Reconcile Docker Containers for Model Serving

DevFeed: [Building a Minimal Control Plane to Reconcile Docker Containers for Model Serving](<https://devfeed.tech/articles/i-killed-a-container-and-it-came-back-40144.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2026-07-02-i-killed-a-container-and-it-came-back/>)

Published: 2026-07-02T00:00:00Z

Content type: tutorial

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [control-plane](<https://devfeed.tech/topics/control-plane.md>), [model-serving](<https://devfeed.tech/topics/model-serving.md>), [Docker Container](<https://devfeed.tech/topics/docker-container.md>), [Amazon Machine Learning](<https://devfeed.tech/topics/amazon-machine-learning.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>)

Tags: [container](<https://devfeed.tech/tags/container.md>), [control-plane](<https://devfeed.tech/tags/control-plane.md>), [inference](<https://devfeed.tech/tags/inference.md>), [model-serving](<https://devfeed.tech/tags/model-serving.md>), [python](<https://devfeed.tech/tags/python.md>)

### AI overview

The author explains control planes by building a small Python-based system that maintains a declared number of Docker containers for model serving. The system repeatedly compares desired and actual state and reconciles differences, including restoring a container after it stops.

### Source excerpt

I can design batch, real-time, and streaming inference -- the data plane. But I'd never built the control plane that manages it. So I built the smallest one I could, from scratch, to finally understand reconciliation: the one idea that separates a control plane from a deploy script.