SaaS Fundamentals Checklist for AI & Machine Learning

Choose metrics that map to user value, for example minimize false positives for fraud detection or maximize top-k recall for search. Instrument conversions and downstream KPIs so you can validate that offline gains translate to real impact.

Track dataset sources, licenses, and consent to avoid legal risk, especially for web-scraped text or images. Maintain a data catalog with metadata and lineage using tools like DataHub or Amundsen.

Identify, redact, or tokenize PII with libraries like Presidio before training or indexing. Implement field-level encryption and retention policies to satisfy GDPR and CCPA.

Run double annotation and adjudication for subjective tasks like toxicity or relevance. Track Cohen's kappa and sample audit batches to calibrate guidelines and reduce noise.

Use DVC, LakeFS, or Delta Lake to pin training and eval data to commits. Store checksums so you can reproduce model outputs and pass audits later.

Generate synthetic examples to balance edge cases, then validate with human review and bias tests. Tag synthetic vs real so you can analyze distribution shifts and overfitting.

Use MTEB for embeddings, HELM style evals for LLMs, and BLEU or ROUGE for summarization and translation. Align domain specific tests to your product tasks like code generation or medical triage.

Create deterministic prompts or seeds, freeze tokenizers, and track package versions. Automate regression checks with MLflow or Weights & Biases and fail the pipeline if metrics degrade.

Probe prompt injection, jailbreaks, and toxicity using libraries like Garak or NeMo Guardrails. Maintain a corpus of tricky prompts and report risk scores before shipping.

Compare retrieval augmented generation, LoRA fine-tuning, and distillation against latency and cost budgets. For small updates, prefer RAG or adapters to avoid full retrains and reduce drift risk.

A/B test candidates behind feature flags and validate that offline metrics predict engagement and support outcomes. Track correlation coefficients across launches to refine your offline suite.

Run outputs through moderation APIs or classifiers for toxicity, PII, and hallucination signals. Block unsafe responses or route to human review with clear audit logs.

Build images that lock GPU driver, CUDA, and library versions to prevent runtime mismatches. Validate with nvidia-smi and a smoke test before rollout.

Store artifacts and metadata in a registry like MLflow or SageMaker. Use progressive traffic shifting with canary or shadow deployments to catch regressions early.

Use KServe or Seldon Core with GPU node pools and Horizontal Pod Autoscaler or KEDA for queue based scaling. Set per model concurrency limits to protect tail latency.

Apply INT8 or FP16 quantization and compile with TensorRT, ONNX Runtime, or BetterTransformer. Measure token throughput and quality trade offs before and after.

Pick Pinecone, Qdrant, Weaviate, or pgvector based on SLA, cost, and filtering needs. Chunk documents with overlap, normalize embeddings, and tune ANN parameters for recall and latency.

Cache frequent prompts and nearest neighbor results with a TTL that respects content freshness. Use feature flags to invalidate caches when knowledge updates land.

Propagate trace IDs from API gateway through retrieval, model, and post processing stages. Attach custom spans for token counts, cache hits, and retries for root cause analysis.

Use Evidently or WhyLabs to monitor feature distributions, embedding drift, and output quality. Trigger retraining or RAG reindexing when drift crosses thresholds.

Define per endpoint p95 and p99 targets, then align autoscaling and queue backpressure to meet them. Alert on burn rates to avoid paging fatigue.

Track GPU minutes, tokens, and vector queries and attribute to customers. Use spend budgets and anomaly detection to catch runaway costs early.

Implement graceful degradation such as cached responses or smaller models when upstream providers fail. Use retry budgets with exponential backoff to stabilize under load.

Maintain a curated set of queries and expected behaviors to run in staging and production. Compare across versions to detect subtle regressions that metrics miss.

Separate customer data at the storage and compute layers with per tenant encryption keys and namespaces. Enforce row level policies and strict IAM roles to prevent cross access.

Store API keys and provider credentials in Vault or cloud KMS. Automate rotation and audit access with short lived tokens.

Map data flows to GDPR, HIPAA, or SOC 2 controls including audit logs and subprocessors. Offer regional routing and storage options to honor residency requirements.

Version breaking changes, expose per key quotas, and enforce fair use with token and request caps. Return actionable error codes and retry after headers.

Instrument per feature consumption like tokens, images, or training hours and tie to billing. Provide real time usage dashboards and alerts to prevent bill shock.

Implement SSO via SAML or OIDC, SCIM provisioning, and detailed audit logs. Support custom model policies, private datasets, and VPC peering for larger accounts.