CI/CD for Machine Learning | MLOps Best Practices

Machine Learning often runs a bit behind software engineering when it comes to automation. The phrase “It runs on my laptop” is terrifying when you’re talking about a production system. To scale AI, we need to adopt MLOps, which basically means applying professional engineering standards to data science. This article looks at how to build reliable automated pipelines.

The Pipeline: Code, Data, and Models

In MLOps, standard CI/CD isn’t enough because ML systems have two moving parts: Code and Data. We need to expand our thinking:

1. Continuous Integration (CI)

Just like with software, we have to test our code.

Linting: Making sure the Python code is clean and readable.
Unit Tests: Checking that our data processing functions actually do what they’re supposed to.
Data Checks: Verifying that the data coming in is the right shape and format.

2. Continuous Training (CT)

This is the cool part. The system automatically re-trains the model when:

Fresh data arrives.
The model’s performance starts to drop.
Someone improves the model’s code.

3. Continuous Deployment (CD)

Automatically putting the new model live.

Canary Deployment: We send just 10% of traffic to the new model first to make sure it’s working well before switching everyone over.
Shadow Mode: We run the new model in the background, making predictions but not showing them to users, just to double-check it’s behaving correctly.

Recommended Tools

For most companies, we recommend a mix of these solid tools:

Git: For storing code.
Kubeflow Pipelines or Apache Airflow: For managing the workflow steps.
MLflow: Think of this as a library for your models, storing different versions and their results.
Kubernetes (GKE/EKS): For the heavy lifting and computing power.

Why Bother?

Setting up a proper pipeline reduces the time it takes to get an idea into production from months to days. It also stops us from accidentally deploying a bad model that could lose money or hurt your reputation.

Alps Agility engineers work side-by-side with your data scientists to build these automated factories, ensuring your AI projects are sustainable and scalable.