| id | SKL-ml-MLSERVING |
| name | Ml Serving |
| description | Model serving deploys ML models for predictions. This guide covers REST APIs, batch prediction, optimization, and deployment strategies for building production ML systems that serve predictions reliab |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
Ml Serving
Skill Profile
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Overview
Model serving deploys ML models for predictions. This guide covers REST APIs, batch prediction, optimization, and deployment strategies for building production ML systems that serve predictions reliably and efficiently.
Why This Matters
- Production Readiness: Proper serving ensures models are production-ready
- Scalability: Well-designed serving infrastructure scales with demand
- Reliability: Robust serving ensures consistent prediction availability
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
- Inputs:
- Trained model artifacts
- Model metadata (version, features, performance)
- Serving configuration (ports, scaling)
- Entry Conditions:
- Trained model available
- Model tested and validated
- Infrastructure provisioned
- Outputs:
- Prediction API endpoints
- Batch prediction jobs
- Model monitoring metrics
- Health check endpoints
- Artifacts Required (Deliverables):
- Model serving API code
- Docker container configuration
- Kubernetes deployment manifests
- Monitoring dashboards
- Acceptance Evidence:
