Systems Analyst: How do you define requirements for an ML pipeline?

ML pipeline requirements — structured approach to ML system design.

Requirements areas:

• Data: Sources, volume, freshness, quality, labeling.
• Model: Accuracy targets, latency, throughput.
• Training: Frequency, compute resources, versioning.
• Deployment: Online vs batch, rollback, monitoring.

Pipeline components:

1. Data ingestion: Streaming or batch collection.
2. Data validation: Schema checks, drift detection.
3. Feature engineering: Transformation, normalization.
4. Training: Model training and hyperparameter tuning.
5. Evaluation: Metrics, A/B testing, fairness checks.
6. Deployment: Model serving (batch, real-time).
7. Monitoring: Performance drift, data quality.

Tools:

• Kubeflow, MLflow, Airflow, SageMaker.
• Feature stores: Feast, Tecton.

Key considerations:

• Reproducibility (version data, code, model).
• Scalability (handle growing data).
• Compliance (explainability, bias detection).