MLOps Fundamentals
MLOps applies DevOps principles to machine learning systems. It provides practices and tools for reliably building, deploying, monitoring, and retraining ML models in production so they remain accurate and maintainable over time.
itArtificial intelligence and machine learning | OpenSkills.info
Intro
MLOps Fundamentals
MLOps is the discipline of putting machine learning models into production and keeping them there reliably. A model that works in a notebook is not a product. MLOps bridges the gap between experimentation and operation.
Why MLOps exists
Machine learning systems fail in ways traditional software does not. The code may be correct, but the model degrades because the world changes. Input data drifts. Upstream schemas break. A model trained on last year's behavior gives wrong answers this year.
Traditional software engineering handles these problems with CI/CD, testing, monitoring, and rollback. MLOps adapts those practices for the specific challenges of ML:
- Models depend on data, not just code.
- Training is expensive and non-deterministic.
- Performance degrades silently without explicit monitoring.
- Reproducing a result requires tracking code, data, configuration, and environment together.
The ML lifecycle
An ML system moves through stages:
This section is part of the paid course.
See pricing to subscribe, or log in if you already have access.