2. Core Objectives

Every ML system design balances five core objectives. Your design choices prioritize some over others depending on the use case.

1. Scalability

The ability to handle growing data volumes, request rates, and model complexity. Measured by throughput (requests/second, predictions/hour) and data throughput (GB/hour through the pipeline).

• Horizontal scaling: Add more servers for inference.
• Distributed training: Spread training across GPUs.
• Partitioned data: Shard feature stores and model replicas.

2. Latency

End-to-end response time from request to prediction. Critical for real-time applications.

Latency is budgeted across components: network → feature fetch → inference → post-processing.

3. Reliability

Measured by uptime percentage and prediction quality consistency. Includes fault tolerance (graceful degradation when components fail) and accuracy stability (model quality does not degrade over time).

4. Adaptability

The ability to respond to changing conditions: data distribution shifts, new user behaviors, business requirement changes.

• Model retraining: Scheduled (daily) or triggered (drift detected).
• Feature adaptability: Add/remove features without retraining everything.
• Configurability: Feature flags, model version routing, A/B experiments.

5. Explainability & Fairness

Increasingly non-negotiable for regulated industries. Explainability means understanding why a model made a particular prediction. Fairness means ensuring the model does not discriminate against protected groups.

• Explainability tools: SHAP, LIME, feature importance, attention visualization.
• Fairness metrics: Demographic parity, equal opportunity, disparate impact.
• Bias detection: Monitor prediction rates across demographic groups.

# Example: fairness monitoring
Group A (majority): approval rate = 72%
Group B (minority): approval rate = 68%
Disparity: 4% → acceptable (threshold: 5%)
If disparity > 5%: alert for investigation