SaaS Fundamentals Checklist for AI & Machine Learning

Use DVC or lakeFS to version raw data and annotations and store immutable artifacts in S3/MinIO; reference commit hashes in training and inference to ensure experiments are reproducible.

Validate schemas and distributions with Great Expectations or TFDV before training, then run Evidently to detect covariate and target drift on production feeds and alert on threshold breaches.

Scan and redact PII using Microsoft Presidio or spaCy patterns during ingestion and before logging; keep raw sensitive data in a restricted enclave with per-tenant KMS keys.

Adopt Feast or a managed feature store to align online and offline features, define TTLs and fill strategies, and backfill consistently so models see identical logic in training and serving.

Use SDV or Gretel to create synthetic examples for rare scenarios, then validate utility and privacy with k-anonymity or differential privacy metrics before mixing with real data.

Document sources, licenses, collection dates, filters, and known biases with a data sheet and maintain lineage graphs; require Git-based approvals for any dataset changes.

Start with a baseline that matches your latency and accuracy constraints, for example RAG with an instruction-tuned open model before full fine-tuning, to validate product fit quickly.

Pin Docker images, CUDA/cuDNN versions, and random seeds; track params, code, and artifacts with MLflow or Weights & Biases so you can reproduce any model snapshot exactly.

Create task-specific evals with golden prompts, expected outputs, and adversarial cases; use Ragas or OpenAI Evals for LLMs and integrate pass/fail gates into CI.

Measure subgroup metrics with Fairlearn or Aequitas, set guardrails for disparate impact, and require sign-off when any protected attribute shows a regression.

Apply LoRA/QLoRA adapters and quantize to int8/int4 via bitsandbytes or TensorRT to reduce memory and cost while preserving quality on target benchmarks.

Document intended use, training data, metrics, limitations, and safety mitigations in a model card and link it from API docs and the admin console.

Build minimal CUDA-enabled images and enable mixed precision (fp16/bf16) to reduce memory and latency; validate on the same GPU class as production.

Adopt vLLM or Text Generation Inference for LLMs, or NVIDIA Triton for batchable multimodel serving, to leverage dynamic batching and paged KV cache for throughput.

Implement request batching and priority queues and scale with Kubernetes HPA/KEDA using GPU utilization, queue depth, and p95 latency as signals.

Index content with FAISS, pgvector, or Pinecone using sensible chunking and metadata filters; cache embeddings and responses and invalidate on content updates.

Route a slice of traffic to candidates with Argo Rollouts or Flagger and automatically roll back if error rates, latency, or quality scores degrade.

Profile with Nsight Systems and PyTorch profiler, choose L4/A10 for lighter loads and A100/H100 for high throughput, and leverage spot or reserved instances where safe.

Apply per-tenant encryption keys with KMS, row-level security, and VPC boundaries; route storage and inference to the customer's selected region to meet data residency needs.

Support SSO via OIDC/SAML, SCIM for provisioning, and implement granular RBAC with MFA; separate permissions for projects, datasets, models, and billing.

Log data reads, model deployments, permission changes, and admin actions with signed, immutable storage and retention aligned to SOC 2 and ISO 27001 controls.

Sandbox tool use, strip hidden markup, enforce allowlists for connectors, and scan inputs/outputs for toxic or sensitive content with classifiers or regex rules.

Store SPDX identifiers and license metadata with each dataset and model, block restricted content from training, and include license checks in CI.

Define on-call rotations and runbooks for model drift, abuse, hallucination spikes, and data leakage, with clear rollback and feature-flag procedures.

Meter tokens, requests, or compute-seconds with OpenMeter or an internal meter and sync to Stripe or Paddle for prepaid, postpaid, and committed-use plans.

Enforce quotas and rate limits at the API gateway (Kong, Envoy, API Gateway) and return remaining-usage headers so customers can manage consumption.

Expose SHAP/LIME explanations for tabular models and show citations with confidence for LLM RAG responses so analysts can verify outputs.

Capture thumbs up/down with reason codes and free-text notes in-app and store labeled examples to build RLHF or preference datasets for iterative improvement.

Offer private networking options like VPC peering or PrivateLink, dedicated instances, custom SLAs, and optional customer-managed encryption keys.

Provide validated system prompts, tool schemas, and guardrails plus Python/JS SDK examples and Postman collections to shorten time-to-first-success.