MLOps: Machine Learning in Production
0 of 18 lessons complete (0%)
Exit Course
ML Pipelines and Orchestration
Why Notebooks Do Not Scale to Production
Preview
Orchestrating Workflows with Airflow and Prefect
Kubernetes-Native Pipelines with Kubeflow
3 lessons
Experiment Tracking
The Case for Tracking Every Run
Logging Runs with MLflow
Collaboration Dashboards with Weights and Biases
3 lessons
Versioning Registries and CI/CD
Model Versioning and the Registry Pattern
CI/CD Pipelines for ML Code and Models
Deployment Strategies from Shadow to Canary
3 lessons
Containers and Kubernetes for ML
Docker Images for Training and Inference
Running ML Workloads on Kubernetes
GPUs Autoscaling and Resource Management
3 lessons
Feature Stores and Data Versioning
Why Features Need a Store
Offline and Online Feature Serving
Data Versioning with DVC
3 lessons
Production Monitoring
Monitoring ML Systems Beyond Uptime
Detecting Data Drift in Production
Alerting Retraining and Incident Response
3 lessons
Feature Stores and Data Versioning
Offline and Online Feature Serving
You don’t have access to this lesson
Please register or sign in to access the course content.
Take course
Sign in
Previous
Next