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Deadline to Die

Phase 6: Governance

Phase 6: Governance & Scale

In the final phase, we manage Machine Learning at the enterprise level, focusing on compliance, efficiency, and infrastructure.

🟢 Level 1: The Feature Store

In a large company, many models use the same features. A Feature Store (e.g., Feast) provides a central repository for “Pre-calculated Features.”

  • Offline Store: For training (Parquet/Spark).
  • Online Store: For inference (Redis/Cassandra).

🟡 Level 2: Model Explainability & Fairness

We must prove why the model made a decision and ensure it’s not biased.

1. Explainability (XAI)

  • SHAP: Quantifies the contribution of each feature to a specific prediction.
  • LIME: Local interpretable model-agnostic explanations.

2. Fairness

  • Bias Detection: Auditing model performance across different demographics (Gender, Race, Age).

🔴 Level 3: Infrastructure at Scale (Kubernetes)

3. Kubeflow and Seldon

Kubernetes is the OS of the modern cloud. Kubeflow is the toolkit for ML on K8s.

  • Components: Pipelines, Notebooks, and Training Operators.

4. GPU Virtualization

Sharing expensive GPUs across multiple training jobs using NVIDIA Multi-Instance GPU (MIG).