Phase 714 DaysAdvanced
Phase 7 — System Design & MLOps
Deploy, monitor, and continuously improve AI systems in production — covering serving APIs, containers, CI/CD, drift detection, and cost governance.
- Ship a production-grade FastAPI service with validation, Docker, and health checks.
- Set up MLflow experiment tracking and post-deployment monitoring.
- Build CI/CD pipelines that lint, test, and deploy on every commit.
⚡ Must Know
- FastAPI — async endpoints, Pydantic validation
- Docker — Dockerfile, images, containers
- Model Serialization — ONNX, safetensors, joblib
- MLflow — experiment tracking, model registry
- GitHub Actions — CI/CD pipelines
- Model Monitoring — data drift, concept drift
- Data Versioning — DVC
- Cloud Basics — AWS/GCP (S3, EC2, Cloud Run)
- Latency + Throughput optimization
- A/B Testing for models
✨ Good to Know
- Kubernetes basics
- Prometheus + Grafana monitoring
- Weights & Biases — W&B Sweeps
- Serverless — Lambda, Cloud Run
- Apache Airflow — data orchestration
- Cost optimization — batching, caching, quantization
📚 Resources
FastAPI Tutorial
Official tutorial — the fastest way to learn production API patterns.
fastapi.tiangolo.com ↗Full Stack Deep Learning
Best end-to-end coverage of production ML engineering.
fullstackdeeplearning.com ↗🏗️ Projects
ML REST API (Deployed)
Model-serving API with validation, containerization, and guardrails.
LLM App + Monitoring
LLM app with MLflow traces, latency metrics, and quality drift tracking.