The whole picture
An ML platform is the integrated system that ties together the components covered across MLOps so teams ship and operate models reliably. Each piece exists in isolation, but their value comes from how they connect.
The layers
- A data layer with versioned datasets and a feature store provides consistent, traceable inputs.
- A training layer with pipelines and experiment tracking produces and records candidate models.
- A registry governs which versioned model is staging or production.
- A serving layer with containerized inference servers answers requests.
- A monitoring layer watches drift and performance and feeds the retraining trigger.
The feedback loop
The defining feature of a mature platform is that monitoring closes the loop back to training. Drift or decay triggers retraining, the candidate is gated and registered, deployment patterns roll it out safely, and monitoring resumes. The system continuously improves itself.
Design principles
- Reproducibility runs through every layer via versioned data, code, and models.
- Automation removes manual handoffs that cause errors and delay.
- Observability makes every stage measurable so failures are caught early.
Key idea
An ML platform integrates data, training, registry, serving, and monitoring into a closed loop where monitoring triggers retraining, built on reproducibility, automation, and observability.