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.
Projects
Beginner
| Project | Description | Time |
|---|---|---|
| Model Serving | Deploy models with FastAPI | ~2 hours |
| Docker Deployment | Containerize AI applications | ~3 hours |
Intermediate
| Project | Description | Time |
|---|---|---|
| LLM Caching | Reduce costs with intelligent caching | ~4 hours |
| Monitoring Dashboard | Track model performance | ~6 hours |
| A/B Testing | Experiment with model versions | ~4 hours |
Advanced
| Project | Description | Time |
|---|---|---|
| Complete Pipeline | End-to-end ML pipeline with CI/CD | ~5 days |
| Auto-Scaling | Handle traffic spikes automatically | ~4 days |
| Model Registry | Version control for ML models | ~4 days |
Key Concepts
- Containerization and orchestration
- Model versioning and registry
- Monitoring and observability
- Cost optimization
- Scaling strategies