AI Tutorials
MLOps & AI Engineering
Master the full MLOps stack — project lifecycle, data versioning, experiment tracking, feature stores, model serving, CI/CD, production monitoring, A/B testing, cost optimization, and end-to-end platform architecture.
10 chapters · 246 min
Getting models to production reliably — build, deploy, monitor, and maintain
- Ch. 01Read →
ML Project Lifecycle
Problem framing, feasibility, success metrics, and CRISP-DM
beginner · 22 min
- Ch. 02Read →
Data Versioning & Pipelines
DVC, Airflow, Prefect, and Kedro for reproducible data pipelines
intermediate · 25 min
- Ch. 03Read →
Experiment Tracking
MLflow, Weights & Biases, and Neptune for runs, metrics, and artifacts
intermediate · 22 min
- Ch. 04Read →
Feature Stores
Feast, Tecton, and Hopsworks for online/offline serving and point-in-time correctness
intermediate · 24 min
- Ch. 05Read →
Model Serving & Inference
FastAPI, TorchServe, Triton, ONNX, batching, and latency optimization
intermediate · 26 min
- Ch. 06Read →
CI/CD for Machine Learning
Model testing in pipelines, GitHub Actions, and automated retraining triggers
intermediate · 24 min
- Ch. 07Read →
Production Monitoring
Data drift, concept drift, Evidently, Arize, and Fiddler
intermediate · 25 min
- Ch. 08Read →
A/B Testing & Experimentation
Statistical power, multi-armed bandits, holdback groups, and interleaving
advanced · 26 min
- Ch. 09Read →
Cost Optimization & Infrastructure
GPU spot instances, auto-scaling, right-sizing, and cost-per-prediction
advanced · 24 min
- Ch. 10Read →
ML Platform Architecture
Feature platform, model registry, serving layer — end-to-end reference design
advanced · 28 min