MLOps: Machine Learning in Production
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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
Experiment Tracking
The Case for Tracking Every Run
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