MLOps
The practices and tooling that take a machine-learning model from a notebook to reliable production: experiment tracking, model registries, feature stores, CI/CD for ML, serving, and drift monitoring. Builds on this library's docker-and-containers, kubernetes-fundamentals, harness, opentelemetry, and testing-in-software-engineering courses for the general infra/CI/testing/observability mechanics this course specializes for ML — see each for the foundation.
mlopsmachine-learningdevopsdata-scienceintermediate
24decks
47main cards
240practice cards
0%with images
v1.0.0version
Card type mix
- multiple choice: 108
- true false: 87
- select blank: 51
- multi select: 18
- match pairs: 7
- categorize: 6
- order: 5
- code fill: 3
- command output: 2
What's covered
- What MLOps Actually Solves
- The ML Lifecycle Loop
- Reproducibility Fundamentals
- Data Versioning
- Experiment Tracking Fundamentals
- MLflow Tracking
- Weights & Biases
- Model Registry & Lifecycle Stages
- Feature Store Fundamentals
- Feast in Practice
- Pipeline Orchestration Fundamentals
- Kubeflow Pipelines
- Airflow and Prefect for ML
- CI for ML: Data and Model Quality Gates
- Continuous Training and Retraining Triggers
- Packaging a Model for Deployment
- Model Serving Patterns
- Model Serving Tools
- Safe Rollouts: Canary and Shadow Deployment
- Monitoring ML Systems in Production
- Data Drift and Concept Drift
- Testing ML Systems
- Model Governance and Model Cards
- LLMOps: What Changes for Large Language Models
Example cards
multiple choice
What gap does MLOps close?
order
Order the ML lifecycle loop steps.
Latest: Initial release — 24 decks, each pack with a preview card teaching the concept before its graded main card and practice cards.