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Machine Learning
MLOps Architecture
Microservices ML Platform
Decomposes the ML platform into independent services: feature store, experiment tracking, training, model registry, serving gateway, monitoring.
Intent & Description
📋 Context
The microservices pattern enables scaling to many teams and models with independent deployment and scaling of each component.
Real-world Use Case
Organizations with many teams and models needing independent scaling and best-of-breed tool selection per service.
Source
Advantages
- Scales to many teams and models
- Independent deployment and scaling
- Best-of-breed tool selection
- Clear interfaces
Disadvantages
- Operational complexity
- Requires platform engineering expertise
- Higher infrastructure cost
- Network overhead
Implementation Example
# Microservices ML Platform
feature_store = FeatureStoreService()
experiment_tracker = ExperimentTrackingService()
training = TrainingService()