Master the end-to-end machine learning lifecycle. Build CI/CD pipelines, model registries, monitoring systems, and production-grade ML infrastructure.
Build automated ML pipelines with GitHub Actions, GitLab CI, or Jenkins. Implement testing strategies for data, models, and code.
Implement model versioning, lineage tracking, and artifact management with MLflow or Weights & Biases.
Monitor model performance, data drift, and concept drift. Set up alerting and dashboards with Prometheus and Grafana.
Design and implement feature stores with Feast or Tecton. Enable feature reuse, versioning, and online/offline consistency.
Design and analyze A/B tests for ML models. Understand statistical significance, power analysis, and multi-armed bandits.
Orchestrate complex ML workflows with Kubeflow Pipelines, Airflow, or Prefect. Handle dependencies, retries, and parallelism.
Deploy and manage ML workloads on Kubernetes. Use KServe, Seldon, or custom operators for model serving.
Implement data drift, concept drift, and prediction drift detection. Automate retraining triggers.
Configure horizontal and vertical pod autoscaling for inference workloads. Optimize latency and throughput.
Debug production ML issues: model degradation, data quality problems, infrastructure failures. Root cause analysis techniques.