# Microservices ML Platform

> Decomposes the ML platform into independent services: feature store, experiment tracking, training, model registry, serving gateway, monitoring.

- **Category**: Machine Learning
- **Subcategory**: MLOps
- **Canonical URL**: https://designpattern.fyi/machine_learning/microservices_ml_platform/

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## Description
**Context**: The microservices pattern enables scaling to many teams and models with independent deployment and scaling of each component.


## Use Cases
Organizations with many teams and models needing independent scaling and best-of-breed tool selection per service.



## Implementation Example

```python
# Microservices ML Platform
feature_store = FeatureStoreService()
experiment_tracker = ExperimentTrackingService()
training = TrainingService()
```



## Trade-offs


### Advantages

- - Scales to many teams and models

- - Independent deployment and scaling

- - Best-of-breed tool selection

- - Clear interfaces




### Considerations & Drawbacks

- - Operational complexity

- - Requires platform engineering expertise

- - Higher infrastructure cost

- - Network overhead







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**Reference**: [Original Source](https://kindatechnical.com/mlops-guide/mlops-architecture-patterns-and-reference-designs.html)