MLOPS Tutorial

MLOps (Machine Learning Operations) is a set of practices that combines: Machine Learning (ML) DevOps Data Engineering to build, deploy, monitor, and manage machine learning models efficiently in production environments.

4.6 (270)9,470 learners22 lessons1h 36m

Curriculum

Topic

From Notebook Chaos to Autonomous Retraining: The 4 Levels of MLOps Maturity3:50
The Reproducibility Receipt: Proof Your ML Results Can Be Trusted4:26
MLflow 3.x: One Registry for XGBoost, GPTs, and Everything Between4:07
Git for Data: DVC, lakeFS, and the End of “Works on My Laptop4:24
When MLflow Stops Scaling: Choosing Between W&B, Comet, and Neptune3:58
Training-Serving Skew: The Silent Model Killer at 3 A.M3:49
feast vs tecton vs hopsworks — the feature store choice you'll live with for 5 years3:16
The Sub-10ms Inference Stack: How Redis & Kafka Deliver Features at Lightning Speed5:18
The Orchestrator Wars: Kubeflow vs Airflow vs Prefect vs Flyte3:59
CI/CD for Models: GitHub Actions That Train, Test, and Block Bad Deploys4:13
The Death of Nightly Retraining: Drift-Aware, Event-Driven ML Pipelines5:35
Batch vs Online vs Streaming Inference: The Choice That Shapes Your Cloud Bill3:30
The Serving Stack Showdown: BentoML vs KServe vs Seldon vs Triton4:14
Shadow Traffic, Canaries & Ramp-Ups: Deploy Models Without Breaking Production4:36
100 Models, One GPU, Zero Meltdowns: The Multi-Model Endpoint Pattern4:28
Three Flavors of Drift: Data, Concept & Prediction—and How to Fix Each One3:25
The Silent Failure Stack: Evidently vs Arize vs Fiddler vs WhyLabs5:31
Model Cards, Lineage Logs & the 2 A.M. Audit Call You Didn’t See Coming4:27
Prompts as Code: Versioning, Evals & A/B Testing the New Model Artifact4:27
RAG in Production: The Chunking, Embedding & Vector DB Decisions That Make or Break Recall4:48
Catching Hallucinations Live: The Guardrail Stack That Keeps LLMs Honest6:13
AgentOps & Trace Stores: Debugging the Agent That Fails on Step Seven3:08