MLOps
MLOps & Deployment
Deploy and scale AI systems in production
MLOps & Deployment
Take your AI projects from prototype to production. Learn deployment, monitoring, and scaling strategies.
Why MLOps?
Most ML projects never make it to production. The gap between a working notebook and a reliable production system is where MLOps lives.
| Challenge | What Happens Without MLOps | What MLOps Provides |
|---|---|---|
| Deployment | Manual, error-prone releases | Automated CI/CD with validation gates |
| Monitoring | Silent model degradation | Real-time drift detection and alerts |
| Scaling | Fixed resources, wasted spend | Auto-scaling based on demand |
| Versioning | "Which model is in prod?" | Registry with stage management |
| Cost | Uncontrolled API spending | Caching, routing, and cost tracking |
Projects
Beginner
| Project | Description | Time |
|---|---|---|
| Model Serving | Deploy models as REST APIs with FastAPI, health checks, and Prometheus metrics | ~2 hours |
| Docker Deployment | Containerize AI applications with multi-stage builds and Docker Compose | ~3 hours |
Intermediate
| Project | Description | Time |
|---|---|---|
| LLM Caching | Reduce LLM costs 40-60% with semantic similarity caching | ~4 hours |
| Monitoring Dashboard | Track model performance, latency, and data drift with Prometheus and Grafana | ~6 hours |
| A/B Testing | Statistically compare model versions with traffic splitting and bandits | ~4 hours |
Advanced
| Project | Description | Time |
|---|---|---|
| Complete Pipeline | End-to-end ML pipeline with CI/CD, MLflow, and blue-green deploys | ~5 days |
| Auto-Scaling | Handle traffic spikes with HPA, KEDA, and request batching | ~4 days |
| Model Registry | Version control for ML models with stage transitions and artifact storage | ~4 days |
Case Studies
Real-world implementations showing MLOps practices in production.
| Case Study | Industry | Description | Status |
|---|---|---|---|
| LLM Serving at Scale | Technology | Production LLM gateway with routing and observability | Available |
Key Concepts
- Containerization and orchestration
- Model versioning and registry
- Monitoring and observability
- Cost optimization
- Scaling strategies