Requirements Gathering and Scoping Questions

Requirements:

• Functional: 10k QPS globally, 10ms median inference SLO, 99.99% availability.
• Constraints: multi-region (geo-proximity), strong feature consistency for inference inputs, safe model rollout.

High-level architecture:

• Global DNS + Geo-routing -> Regional Edge LB (CDN / Regional ALB) -> Regional API Gateway -> Per-region Model Serving Cluster (K8s/EC2 with GPU/CPU pods) -> Feature Store + Cache -> Central Control Plane (model registry, rollout manager, metrics).

Key components & choices:

1. Model replication

• Maintain one active model per region (replicated binaries/artifacts from central registry to minimize cross-region RTT).
• Use immutable model artifacts (S3/GCS + signed manifests). Pull by regional cluster on deployment.
• Keep model weights local to serving nodes (warm containers) to meet 10ms median.

2. Feature consistency

• For low-latency, perform feature lookup locally: use regional Feature Store (Redis/Memcached for hot features) populated via async CDC from primary datastore.
• For features requiring global consistency (user profile updates), use last-write-wins with per-request version/timestamp and fall back to strongly-consistent read only when necessary (rare), accepting slightly higher tail latency.

3. Model update propagation (rolling/canary)

• Central control plane triggers staged rollout:
  • Canary: deploy to small % of nodes in one region; run shadow traffic and A/B eval.
  • Metrics-driven promotion: automated MLOps pipeline checks latency, accuracy, resource usage, and rollback on anomaly.
  • Rolling updates across pods then regions to prevent simultaneous multi-region risk.
• Use traffic-splitting at the API gateway for canary.

4. Cross-region failover

• Active-active regions with geo-routing; failover via health checks + DNS TTLs (short TTL ~30s) and client-side retry with region fallback.
• Replicate state (models, feature snapshots) asynchronously; ensure idempotent requests and sticky session avoidance.

5. Storage choices

• Model artifacts: object store (S3/GCS) + CDN for quicker pulls.
• Feature store: regional Redis clusters for hot reads, regional OLAP for cold features (Bigtable/Spanner/Cassandra) with CDC pipeline.
• Metadata: central SQL/NoSQL (highly available) for registry and rollout state.

Scoping trade-offs (per-region vs global model)

• Per-region models (region-specific tuning): lower latency, better locality, compliance benefits; higher cost (multiple training/validation runs, storage, ops complexity) and model divergence risk.
• Single global model: simpler ops, consistent behavior, lower training cost, but may increase latency if inference routed cross-region or require larger model to cover all distributions.
  Recommendation: hybrid — single global model replicated per-region for serving; optionally region-adapted fine-tuning overlays (small adapter layers) to balance cost and accuracy.

Scaling & reliability:

• Provision capacity with headroom (10k QPS -> per-region partitioning, autoscale pods using HPA/metrics), use request batching where possible, GPU pooling for heavy models.
• SLOs achieved by local replicas, warm pools, aggressive caching, and circuit-breakers for downstream feature stores.

Metrics & monitoring:

• Track per-region p50/p99 latency, error rates, feature freshness, model drift, rollout KPIs. Automated rollback when safety thresholds breached.

This design prioritizes low-latency and availability by keeping serving and hot features regional, while centralizing control for safe, observable rollouts and cost-effective training.

1. KPI: Click-Through Rate (CTR) on recommendations

• Numerator: number of recommendation impressions that received at least one click.
• Denominator: total number of recommendation impressions.
• Instrumentation: emit deterministic impression and click events with recommendation_id, user_id, position, timestamp; deduplicate server- and client-side; store in event pipeline (Kafka → analytics DB) and compute daily/weeky aggregates. Tie clicks to the exact recommended item via unique impression_id.
• Pitfall/gaming: models may surface clickbait or sensational items that increase clicks but reduce downstream satisfaction.

2. KPI: Short-term Retention (session continuation rate post-recommendation)

• Numerator: number of sessions where a user performed at least one meaningful action (e.g., view > X sec, add-to-cart, watch > 50%) within 10 minutes after engaging with a recommended item.
• Denominator: number of recommendation engagements (clicks) in the period.
• Instrumentation: track engagement events with timestamps and content metadata; compute time-window joins to attribute follow-up actions to the preceding recommendation click.
• Pitfall/gaming: publishers could create auto-play or micro-interactions that artificially inflate “meaningful” actions.

3. KPI: Long-term Engagement Lift (7-day active users attributable to recommendations)

• Numerator: number of distinct users who were active (>=1 meaningful session) within 7 days after being exposed to recommendations and were not predicted to be active without them (requires uplift or attribution model).
• Denominator: number of distinct users exposed to recommendations.
• Instrumentation: use randomized holdout or causal inference (AB test with holdout) to attribute incremental DAUs; log exposures and downstream activity; compute uplift.
• Pitfall/gaming: segmentation or sample leakage across treatment/control can bias uplift estimates.

4. KPI: Satisfaction / Quality Rate (positive feedback rate)

• Numerator: number of recommendation interactions with explicit positive feedback (like, thumbs-up, save) or high implicit satisfaction signal (long dwell > threshold).
• Denominator: total number of recommendation interactions (clicks or impressions depending on product).
• Instrumentation: capture explicit feedback events and normalized dwell times per content type; combine into a single labeled satisfaction flag using deterministic rules; back this with periodic user surveys for calibration.
• Pitfall/gaming: users might be prompted to give positive feedback (UI nudges) or incentives could induce biased responses.

Notes: ensure consistent event schemas, deterministic impression IDs, server-side recording where possible, and regularly validate telemetry (QA tests, ingestion alerts, and replay checks) to prevent measurement drift or loss.

Requirements checklist — migrating an on‑prem ML pipeline to a managed cloud platform

Scope & constraints

• List pipeline components (data sources, feature store, training infra, model registry, inference endpoints, orchestration, logging, secrets).
• Compliance/regulatory constraints (data residency, GDPR, HIPAA), SLAs (latency, availability), budget.

Data transfer validation

• Inventory data types, schemas, sizes, and change rate.
• Choose transfer method (VPN/DirectConnect, Snowball, streaming via Kafka/Cloud PubSub).
• Validate integrity: checksums, row counts, schema validation tests after transfer.
• Performance tests: measure bandwidth, initial bulk seed time, ongoing sync lag.
• Acceptance: zero data loss on parity checks; <X minutes lag for streaming.

Security & compliance checks

• Network: private VPC, least-privilege firewall rules, VPC endpoints for storage.
• Identity: RBAC/IAM policy mapping from on‑prem roles to cloud roles.
• Data at rest/in transit encryption (KMS-managed keys), key rotation policy.
• Secrets management: migrate to cloud secret store (HashiCorp/Secret Manager).
• Audit & logging: enable audit trails, retain logs per compliance retention rules.
• Compliance evidence: run security scan, provide artifacts for compliance officers.

Latency impact assessment

• Identify latency-sensitive components (online inference, feature fetch).
• Baseline on‑prem latencies; run synthetic and canary tests from client regions to cloud endpoints.
• Evaluate network hop cost; consider hybrid (edge, cloud regions, regional caches).
• Acceptance: latency within SLA or design mitigations (edge caching, model quantization).

Model parity tests

• Define parity suite: unit tests, offline evaluation, shadow/parallel A/B tests.
• Reproduce training environment (same framework versions, hardware/GPU types, random seeds).
• Validate numerical parity: metrics within acceptable delta (precision/recall A/B).
• Inference parity: run identical input batches through on‑prem and cloud models; compare outputs and performance.
• Statistical significance testing and roll-forward gating.

CI/CD adjustments

• Update pipelines to cloud build systems (Cloud Build, Jenkins, GitHub Actions with cloud runners).
• Containerize training/serving, store artifacts in cloud registry.
• Automate infra-as-code (Terraform/CloudFormation) for reproducible environments.
• Add deployment gates: model tests, canary rollout, automatic rollback triggers.
• Ensure dev/test/prod isolation and approval workflows.

Monitoring replication

• Mirror on‑prem metrics: model performance (drift, accuracy), infra (CPU/GPU, memory), data quality (schema drift, null rates), request/response metrics.
• Integrate with cloud observability (Prometheus/Cloud Monitoring, logging, alerts).
• Implement anomaly detection and alert playbooks; dashboard parity for stakeholders.
• Retention & cost tuning for logs/metrics.

Rollback & recovery plan

• Maintain immutable model artifacts and versioned infra templates.
• Define rollback triggers (metric degradation thresholds, increased errors).
• Practice runbooks: automated rollback (traffic shift back), manual escalation flow.
• Backup plan for data and feature store snapshots; RTO/RPO targets defined.

Cost impact assessment

• Estimate ongoing costs: storage, data egress, compute (training/serving), managed services, monitoring retention.
• Run cost simulations with expected traffic and training cadence; include peak scenarios.
• Identify cost controls: autoscaling, reserved instances, data lifecycle policies, budget alerts.
• Acceptance: cost within budget or identified trade-offs documented.

Validation & cutover plan

• Stage migration (pilot subset, shadow mode, canary, full cutover).
• Success criteria per stage (data parity, latency, model metrics, error rates).
• Rollback window and post-mortem scheduled.

Deliverables

• Migration runbook, test plans, IAM mappings, cost model, monitoring dashboards, rollback playbooks, compliance artifacts.

This checklist should be adapted to your org’s SLAs, compliance, and budget; for each item assign owner, test, acceptance criteria, and rollback action.

Requirements (non-functional) — list and rationale:

• Data residency: store/process PHI only in approved jurisdictions; use region-restricted cloud tenants to meet GDPR data transfer and HIPAA state requirements.
• Encryption: AES-256 at-rest; TLS 1.2+ in-transit; customer-managed keys (BYOK) for critical PHI.
• Access controls / RBAC: least-privilege roles, multi-factor auth for admins, just-in-time elevation, separation of duties, SCIM integration for SSO.
• Audit trails & logging: immutable, tamper-evident logs of data access, model predictions, config changes with retention for regulatory windows; alerts for anomalous access.
• Model interpretability & transparency: per-request explanations (SHAP/Integrated Gradients) for high-risk decisions, model card + data provenance for audits.
• Retention & deletion policy: configurable retention windows, secure deletion workflows, and proof-of-deletion for GDPR Right-to-ERASURE and HIPAA disposal guidance.
• Data minimization & anonymization: minimize PHI used in training; use pseudonymization, differential privacy for analytics.
• Monitoring & incident response: real-time monitoring, IR runbooks, 72-hour breach notification pipelines aligned to GDPR/HIPAA.
• Testing & assurance: regular penetration tests, red-team, and privacy impact assessments (DPIA).

Validation / Compliance:

• Technical: automated compliance checks (IaC linters), encryption/key audits, RBAC policy tests, E2E integration tests that assert data never leaves allowed regions.
• Processual: DPIA, DPA contracts with processors, BAA signed, vendor risk assessments, documented SOPs.
• Evidence: immutable logs, model cards, test reports, pentest/VA reports, deletion receipts, and external audit reports (SOC2/HIPAA readiness).
• Independent audit: yearly third-party audit and continuous internal compliance dashboard.

Impact on scope & timelines:

• Adds upfront time for legal/vendor agreements, infra setup (region-restricted tenants, KMS BYOK) — estimate +4–8 weeks.
• Engineering: implementing RBAC, audit pipelines, and explainability hooks +2–6 weeks; testing & pentest cycles +2–4 weeks.
• Iterative: require privacy-preserving model experimentation (adds training complexity and compute cost) and separate staging with synthetic data.
• Risk buffer: include compliance review gates in milestones; plan for rollback/mitigation windows for incidents.
  Overall: budget ~15–30% more time and 20–40% higher infra/operational costs; schedule explicit compliance milestones and deliverables in PRD.

Situation: We need a reproducible plan to collect representative ground truth across multiple user segments so models are accurate and fair.

Stratification strategy

• Primary strata = user segments relevant to downstream fairness/performance (e.g., geo, device, age-group, power-users vs casual).
• Create mutually exclusive strata by crossing up to 2–3 highest-impact variables (avoid explosion).
• Within each stratum, sample randomly from recent production traffic, then apply quota/weighting to reflect population or to purposely oversample rare-but-critical strata (collect weights for re-weighting during training/eval).

Minimum sample size per stratum (statistical power)

• For estimating a proportion with margin-of-error E at confidence 1−α, use:
  n = (Z_{1−α/2}^2 * p*(1−p)) / E^2.
  Use conservative p=0.5 if unknown. For 95% CI (Z≈1.96) and E=0.03 → n ≈ 1,067 per stratum.
• To detect difference between two strata proportions with power 1−β and minimum detectable effect (MDE) δ, use two-sample formula:
  n_per_group ≈ [(Z_{1−α/2}+Z_{1−β})^2 * (p1(1−p1)+p2(1−p2))]/δ^2.
  Example: α=0.05, β=0.2, p≈0.5, δ=0.05 → n≈784.
• Practical rule: set a floor (e.g., min 800–1,200 labels) for any stratum used in comparative analysis; oversample rare strata proportionally more if they’re critical.

Labeling quality assurance

• Annotator recruitment: qualification tests with gold set; require ≥80–90% agreement to qualify.
• Primary QA pipeline:
  • Gold (pre-labeled) questions mixed ~5–10% per batch to measure ongoing accuracy.
  • Dual labeling: 20–30% of items labeled by two independent annotators; compute inter-annotator agreement (Cohen’s kappa / Krippendorff’s alpha).
  • Adjudication: items with disagreement go to expert adjudicator or majority consensus (3rd label).
  • Calibration sessions weekly for annotators, update guidelines, track label drift.
  • Monitor per-annotator metrics (accuracy on gold, agreement, throughput); remove low performers.
  • Periodic blind audits (sample labeled externally) to estimate real-world error.
• Track label uncertainty and attach confidence metadata for downstream weighting.

Estimated cost and time (example numbers)

• Assume average labeling time per item = 2 minutes (30 items/hour).
• Labeler cost (including overhead) = $25/hour → cost/label ≈ $0.83.
• For 10 strata × 1,100 labels ≈ 11,000 labels → ~367 hours → cost ≈ $9,200.
• Add dual-labeling for 25%: +2,750 labels → +92 hours → +$2,300.
• Adjudication (10% of original): ~1,100 labels × 3 min = 55 hours → +$1,375.
• Total approximate cost ≈ $12–13k; timeline with 5 annotators concurrently ≈ 2–3 weeks.

Stopping criteria

• Statistical: stop when CI for key metrics within target margin E for each critical stratum, and when differences you care to detect (MDE) are powered.
• Stability: model evaluation metrics (accuracy/F1 per stratum) stabilize across two sequential batches (e.g., <1% change) and bootstrap variance low.
• Diminishing returns: marginal gain in metric/uncertainty reduction per additional 1,000 labels falls below threshold (cost vs benefit).
• Operational: reached max budget or minimum samples per stratum floor.

Other practical notes and alternatives

• Use adaptive sampling: start with pilot (e.g., 200/stratum), estimate p and variance, then compute final n to avoid over-collection.
• For extremely rare strata, use targeted recruitment, synthetic augmentation, or active learning to prioritize high-uncertainty examples.
• Always store sampling weights and metadata so analysis can re-weight back to population distribution.

This plan balances statistical rigor (power, CI), label quality (gold, adjudication), cost/time realism, and practical stopping rules (CI targets + stability + diminishing returns) suitable for production ML workflows.