Product Development Checklist for AI & Machine Learning

Compare a simple heuristic or rules engine to an ML approach, and quantify expected lift. If a rule-based baseline achieves the target metric, defer ML to save cost and complexity.

Pick domain-relevant evaluation metrics like precision@k, F1, BLEU, ROUGE, or hallucination rate, and link them to product outcomes like activation or task success. Set minimum acceptable thresholds before build.

Set p95 latency targets and a max cost per request in dollars or tokens. Use these budgets to choose between hosted APIs, fine-tuning, or on-prem GPUs.

Assess risk levels against the EU AI Act, HIPAA, or sector standards, then align controls like human review and audit logging. Document prohibited outputs and safety requirements early.

Train a quick scikit-learn model or use a zero-shot LLM baseline to establish a performance floor and estimate feasibility. This anchors expectations for the first production iteration.

Specify what constitutes a shippable model, expected failure cases, and escalation paths. Include rollback triggers tied to metric drops or safety violations.

List structured, unstructured, and third-party data, including any model or dataset licenses. Exclude sources with usage restrictions that conflict with commercial deployment.

Use Label Studio, Prodigy, or Scale to codify task definitions and edge cases, then track inter-annotator agreement and adjudicate disagreements. High agreement correlates with better generalization.

Adopt DVC or LakeFS to version raw, processed, and training splits with checksums. Record data lineage so you can reproduce any model from its exact snapshot.

Scan and mask PII using tools like Presidio or OpenAI text-moderation plus custom regex rules. Store secrets and encryption keys in a KMS and restrict raw access by role.

Generate targeted synthetic examples with GPT-4 class models or Synthetic Data Vault to cover rare edge cases. Clearly tag synthetic records to avoid leakage in evaluation.

Add Great Expectations checks on schema, ranges, and class balance, and monitor feature drift in production. Alert when drift exceeds action thresholds.

Decide between hosted foundation models, fine-tuning, or training from scratch by comparing latency, cost, data availability, and IP needs. Document why the chosen path wins.

Select a vector database like Pinecone, Weaviate, Qdrant, or pgvector, then tune chunk sizes and retrieval strategies. Measure retrieval recall@k before testing end-to-end quality.

Use MLflow or Weights & Biases to log code versions, datasets, hyperparameters, metrics, and artifacts. Make experiment runs reproducible with seeded configs.

Adopt Optuna or Ray Tune with early stopping, ASHA, or Bayesian search to optimize key parameters. Cap trials by cost and wall time to avoid overruns.

Enable mixed precision, gradient checkpointing, and use PEFT methods like LoRA for LLMs. For inference, try quantization with bitsandbytes or GGUF and batch requests to lift TPS.

Create golden sets with adversarial cases and measure hallucination, toxicity, and jailbreak resistance using Ragas, DeepEval, or Promptfoo. Automate regression checks in CI.

Pin CUDA, cuDNN, and driver versions, then serve with Triton Inference Server, TorchServe, vLLM, or Text Generation Inference. Provide health checks and warmup routines.

Register models in MLflow Registry, SageMaker, or Vertex AI and promote via stages with automated tests. Block promotion if evaluation metrics or safety checks regress.

Use Kubernetes with KEDA or provider autoscaling to scale on queue depth, tokens per second, or p95 latency. Define resource requests and limits to avoid GPU thrashing.

Emit structured logs with prompt templates, model versions, token counts, and cache hits. Export to Prometheus, Grafana, and OpenTelemetry for traces across retrieval and generation stages.

Cache deterministic completions in Redis and store embedding vectors for popular documents to avoid recomputation. Track cache hit rate and evict by recency and cost.

Canary new models to a small traffic slice, shadow them behind the current version, and monitor deltas. Add circuit breakers and fallback to simpler models on errors or budget breaches.

Expose system prompts, show streaming tokens, and add guidance on limitations and ideal queries. Provide retry with revised prompts and tool-assisted suggestions.

Use Llama Guard, Azure Content Safety, or NeMo Guardrails for input and output filtering, plus JSON schema validation for tool use. Log blocked events for review.

Route low-confidence or high-impact predictions to reviewers with active learning to prioritize uncertain items. Feed approved corrections back into training data.

Define data retention windows, enable customer-managed keys, and document processor obligations. Provide GDPR DSR workflows and SOC 2 aligned access controls and audit logs.

Track per-feature token usage and GPU minutes, then set quotas, rate limits, and budget alerts. Offer usage dashboards and tiered pricing aligned to cost drivers.

Measure downstream outcomes like task completion, resolution time, and CSAT alongside model metrics. Use feature flags for quick experiment turns and log template versions.