How to Build an AI/ML Platform for Regulated Enterprises: Complete Guide

Core Components

• Data layer: Feature store + data catalogue + access controls. Delta Lake or Apache Iceberg for versioned, governed data access.
• Experiment tracking: MLflow, Weights & Biases, or Neptune. Every experiment logged with hyperparameters, metrics, data version, and code version.
• Model registry: Versioned model storage with metadata. Model cards documenting purpose, limitations, bias testing, and approval status.
• Training infrastructure: GPU cluster (on-prem or cloud) with resource quotas, job scheduling (Kubernetes + Kubeflow or Ray), and cost tracking.
• Serving layer: Model serving with A/B testing, canary deployments, and automatic rollback. KServe, Seldon, or cloud-native options.
• Monitoring: Model performance monitoring (drift detection, accuracy degradation), infrastructure monitoring, and business metric correlation.

Start with Governance, Not Tooling

• Model risk framework: Define risk tiers for AI models. Tier 1: safety-critical or high-risk (EU AI Act). Tier 2: financial impact. Tier 3: operational. Tier 4: experimental. Different governance requirements for each tier.
• Data governance: Who can access what data? How is access logged? How are data quality issues tracked? This must exist before any ML pipeline.
• Responsible AI policy: Bias testing requirements, fairness metrics, explainability expectations, human oversight requirements — document before you build.
• Choose your stack: Kubernetes-native (Kubeflow, MLflow, KServe) vs managed (SageMaker, Vertex AI, Azure ML). Trade-off: control vs operational burden.

Reproducibility = Compliance

• Data versioning: Every training run pinned to a specific data version. DVC, Delta Lake time travel, or lakehouse versioning.
• Code versioning: Git commit SHA linked to every experiment. Training code in version control, not notebooks.
• Environment versioning: Container images with pinned dependencies. Reproducible training environments.
• Pipeline orchestration: Kubeflow Pipelines, Airflow, or Argo Workflows. DAG-based pipelines that document the entire workflow from data to deployment.
• Automated testing: Data validation (Great Expectations), model validation (accuracy thresholds, bias checks), integration testing (serving endpoint health).

• Model cards: Standardised documentation for every production model — purpose, training data description, performance metrics, known limitations, bias testing results, intended and prohibited uses.
• Approval workflow: Tier 1-2 models require sign-off from model risk management, data privacy, and business owner before production deployment.
• Audit trail: Complete lineage from training data → model → deployment decision → production serving. Immutable logs.
• Drift monitoring: Automated detection of data drift (input distribution changes) and concept drift (model performance degradation). Alerting and automatic fallback.
• Model retirement: Defined criteria for when models must be retrained or retired. Regulatory requirement under EU AI Act for ongoing monitoring.

• Self-service: Data scientists can spin up experiments, access governed data, and deploy models through self-service workflows — without waiting for platform team tickets.
• Cost management: GPU cost allocation by team/project. Spot instance strategies for training. Right-sizing serving infrastructure. FinOps for ML.
• Multi-tenant isolation: Namespace-level isolation on Kubernetes. Network policies preventing cross-team data access. Resource quotas preventing noisy neighbours.
• LLM/GenAI integration: RAG pipelines, fine-tuning infrastructure, prompt management, LLM evaluation frameworks — the platform must evolve with the technology.