Building an ML CI/CD automation pipeline

Author

Govardhana M K
May 24, 2025

TechOps Examples

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🧠 DEEP DIVE USE CASE

Building an ML CI/CD automation pipeline

While the ambition to leverage machine learning and advanced tech is inevitable, it should start with a basic understanding of the difference between conventional DevOps and ML workflows.

In DevOps, automation is built around shipping code reliably. Source gets committed, builds are triggered, packages are deployed, and systems are monitored. It’s linear and predictable.

In ML, the story doesn’t end at deployment. You deal with evolving data, retraining needs, and shifting accuracy. The automation loop extends beyond code delivery to data feedback, model updates, and continuous improvement.

A typical MLOps architecture looks like this:

1. Code and Data Start Together
Unlike traditional pipelines, ML workflows begin not just with code, but with data. Every training cycle is tied to both the logic you write and the data you use. Versioning both is non-negotiable.

2. Development Is Experiment Driven
The dev branch is where experimentation happens. Multiple training runs, parameter tuning, and feature variations are logged, often with metadata tracking for reproducibility and comparison.

3. CI Validates More Than Syntax
When changes move to the main branch, CI kicks in. But here, CI checks more than code quality. It tests model behavior, validates output shape, and flags performance anomalies or regressions.

4. Models Are Registered, Not Just Built
Trained models are treated as artifacts and pushed to a central registry. This allows models to be tracked, versioned, tested, and promoted across environments like any other deployable asset.

5. Staging Is for Shadow Testing
Before promotion, models are evaluated in a controlled environment using production like data. This is where you catch data drift, check inference latency, and validate business metrics.

6. Production Is Continuous
Once promoted, models are deployed into live environments. But the story doesn’t end there. Feature stores are refreshed regularly, and retraining pipelines may run automatically based on drift or performance triggers.

This architecture turns ML delivery into a structured, reliable process. It creates checkpoints for validation, ensures traceability, and prepares teams to operate ML systems at scale.

This is a good, generic architecture.

Here are three patterns you’ll need to know along with this:

  1. Push Based ML Architecture

  2. Pull Based ML Architecture

  3. Message Based ML Architecture

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1. Push Based ML Architecture

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