Product Development Checklist for AI & Machine Learning

Clarify jobs-to-be-done, success metrics like time saved or CTR, and enumerate unacceptable failures such as toxic generation or high false-positive rates. This anchors modeling, evaluation, and risk controls.

List first-party logs, partner APIs, and public datasets, then set least-privilege access via IAM and network policies. Isolate per-tenant data to prevent cross-customer leakage.

Define annotation rules, edge cases, and tie-breakers, and calibrate with gold sets. Use Label Studio or Prodigy and track inter-annotator agreement to manage label noise.

Validate ranges, nulls, and text anomalies with Great Expectations or Pandera. Block training when contracts fail to avoid cascading errors into models.

Choose task-appropriate metrics like F1, ROC-AUC, BLEU, or p95 latency and link thresholds to churn or conversion. Define both offline and online targets.

Use DVC or LakeFS to snapshot datasets and tie to MLflow runs, and track lineage with OpenLineage or DataHub. This enables reproducibility and rollbacks.

Compare open models versus APIs for latency and cost, and prototype with scikit-learn or compact LLMs. Cap GPU hours to keep iteration focused.

Log code, parameters, datasets, and metrics in MLflow or Weights & Biases with commit SHAs. Use deterministic seeds where feasible.

Apply time series splits for temporal data and stratified splits by label and domain for classification. Partition at the user or session level to prevent leakage.

Use LoRA or other PEFT techniques to tune LLMs on domain data and save adapters separately. Reduce GPU memory while retaining task performance.

Combine rubric-based LLM judges, reference metrics like ROUGE or BLEURT, and small-scale human review. Keep prompt fixtures and seeds constant across runs.

Apply quantization like INT8 or NF4, distillation, or TensorRT compilation to meet latency budgets. Validate on a production-like holdout to monitor degradation.

Select embedding models like E5 or text-embedding-3-large, chunk documents by semantic boundaries, and store vectors in FAISS, Milvus, or Pinecone. Validate recall before tuning generation.

Use explicit instructions, constraints, and examples, and define tool-call schemas. Keep prompts modular and parameterized for quick iteration.

Assign semantic versions to prompts and log outputs and costs. Route a small percentage of traffic to variants and compare quality and latency.

Filter inputs and outputs via moderation APIs and pattern checks, enforce structured tool schemas, and add source grounding. Test with known attack suites.

Use Redis or a feature store to cache frequent queries with TTL, scoped by tenant. Track cache hit rate and token savings.

Set client and server timeouts, jittered retries, and graceful degradation to simpler models or cached answers. Emit fallback telemetry for analysis.

Build images pinned to CUDA and cuDNN versions and validate on GPU-enabled CI. Avoid runtime failures from driver mismatches.

Evaluate Triton, ONNX Runtime, vLLM, or Text Generation Inference based on model type. Enable dynamic batching and token streaming where applicable.

Use HPA or KEDA with metrics like tokens per second and GPU utilization, and leverage spot instances for non-critical workloads. Set concurrency to avoid OOM.

Store artifacts in a registry, sign images with cosign, and scan for CVEs. Restrict egress from inference pods to approved domains.

Mirror traffic to new models in shadow mode and ramp up with feature flags. Track deltas in quality, errors, and cost before full rollout.

Monitor p50-p99 latency, tokens per second, model error codes, and quality scores with Prometheus, Grafana, and OpenTelemetry. Page on sustained SLO breaches.

Enumerate model inversion, extraction, and data poisoning risks, plus abuse vectors. Define layered mitigations and revisit as capabilities evolve.

Detect and redact PII with Presidio or custom rules, tokenize sensitive fields, and avoid unnecessary retention. Apply differential privacy or k-anonymity where required.

Log model versions, prompts, inputs, and outputs with hashed references tied to user IDs. Protect logs with access controls and tamper-evident storage.

Compute Demographic Parity or Equalized Odds and analyze SHAP explanations for feature contributions. Document trade-offs and remediation steps.

Publish model cards and data statements with training sources, limitations, and intended use. List unsafe use cases and escalation paths.

Map data flows to GDPR and CCPA, evaluate HIPAA scope for PHI, and align with SOC 2. Prepare DPIAs for high-risk processing activities.

Provide clear schemas, idempotency keys, pagination, and rate limiting. Document token accounting or compute units so customers can predict spend.

Offer temperature, top-k, and top-p knobs, plus presets for speed versus quality. Persist parameters per request for audit and support.

Route low-confidence or high-impact items to reviewers and use active learning to surface uncertain samples. Feed approved labels into retraining.

Collect thumbs up or down with reason codes, task-specific rubrics, and downstream outcomes. Weight online feedback in scheduled retrains.

Meter tokens, GPU seconds, and storage per tenant with OpenMeter or custom collectors. Expose dashboards so teams can manage spend proactively.

Test usage-based tiers with overage protections and add SSO, SCIM, RBAC, and audit logs for enterprise. Align SLAs with underlying model SLOs.