MLOps

MLOps — Machine Learning Operations — is the set of practices that bridge the gap between ML model development and production deployment. In organizations without MLOps practices, models are developed by data scientists in notebooks, manually handed off to engineering teams for deployment, and rarely updated because the retraining process is too expensive and fragile. In organizations with mature MLOps, model development is version-controlled, training pipelines are automated, deployment is continuous, and model performance is monitored in production with the same rigor as application performance.

The components of a mature MLOps platform include: experiment tracking (recording every model training run with its parameters, metrics, and artifacts), feature stores (managing the feature engineering pipeline to ensure consistency between training and serving), model registries (versioning trained models and managing their lifecycle from development to production to retirement), serving infrastructure (deploying models efficiently at the latency and throughput required), and monitoring (detecting data drift, model degradation, and prediction quality issues in production).

MLOps has direct regulatory implications for AI systems subject to the EU AI Act, FDA guidance on AI/ML-based software devices, or financial services model risk management requirements (SR 11-7). These frameworks require that AI systems be documented, validated, monitored, and updated through defined processes. An MLOps platform that produces experiment tracking records, validation test results, and production monitoring data creates the audit trail these frameworks require — without which organizations cannot demonstrate that their AI systems meet regulatory standards.

We build MLOps platforms for engineering and data science teams — implementing experiment tracking with MLflow or Weights & Biases, building automated training pipelines with Kubeflow or Airflow, establishing model registries with promotion gates that require validation before production deployment, and instrumenting production models for drift detection and performance monitoring. Our implementations generate the audit evidence required for EU AI Act compliance and financial services model risk frameworks.

Compliance-Native Architecture Guide

Design principles and a structured checklist for building software that is compliant by default — not compliant by retrofit. Covers data architecture, access controls, audit trails, and vendor due diligence.