What Is MLOps? Machine Learning Operations Explained

MLOps applies CI/CD and DevOps discipline to machine learning so that data, training, and model deployment are automated, versioned, and reproducible.

MLOps (Machine Learning Operations) is the practice of running machine learning systems in production reliably. It extends the ideas behind CI/CD - automation, versioning, testing, and monitoring - to the messier world of data and models, where a system can break not just from a code change but from a shift in the incoming data.

What MLOps is

MLOps is the set of workflows, tooling, and culture that takes a model from a notebook on a laptop to a service that runs and improves in production. It treats data, code, and trained models as first-class artifacts that all need to be versioned and tested. The goal is the same as DevOps: ship changes quickly and safely, and recover fast when something breaks.

Why ML needs more than CI/CD

A traditional app is defined by its code. An ML system is defined by code, data, and a trained model, and all three can change independently. The same training code can produce a worse model if the data drifts, so MLOps adds data validation, experiment tracking, and model monitoring on top of ordinary continuous integration.

The MLOps lifecycle

A typical loop runs data ingestion, data validation, feature engineering, model training, evaluation, registration of the winning model, deployment, and monitoring. When monitoring detects drift or decay, the loop triggers retraining. Each stage can be a job in a pipeline, which is why CI/CD platforms are the natural home for MLOps.

Running MLOps in CI/CD

Most MLOps stages map cleanly onto CI jobs: validate data on every change, retrain on a schedule, run evaluation as a quality gate, and promote a model only if metrics improve. Training jobs often need GPUs and large memory, and they pull big datasets, so caching and right-sized runners matter a lot for cost and speed.

# A training job in GitHub Actions
jobs:
  train:
    runs-on: gpu-large
    steps:
      - uses: actions/checkout@v4
      - run: dvc pull            # fetch versioned data
      - run: python train.py     # produces model.pkl
      - run: python evaluate.py  # quality gate

Latchkey note

Training and evaluation jobs are heavy and bursty. On Latchkey managed runners you can route training to GPU or large runners, cache multi-gigabyte datasets and base models between runs so you are not re-downloading them every time, and auto-retry flaky data fetches from object storage rather than failing the whole pipeline.

Key takeaways

• MLOps applies CI/CD discipline - automation, versioning, testing, monitoring - to machine learning systems.
• ML systems depend on data, code, and a trained model, so they need data validation and drift monitoring beyond ordinary CI.
• Most MLOps stages map to CI jobs, but training needs GPU or large runners and dataset caching to stay fast and affordable.

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