AI / MLOps Enablement
Take AI and ML models from notebook to reliable, monitored, governed production systems.
Overview
AI and MLOps enablement applies DevOps discipline to machine learning so models move from experiments to dependable production systems. We build pipelines for training, validation and deployment, set up model and data versioning and a model registry, and add monitoring for performance, drift and cost, plus the governance and security controls that responsible AI in production requires.
Methodology & Standards
MLOps practices including CI/CD for machine learning, model registry and versioning, and monitoring for drift and performance, aligned with the NIST AI Risk Management Framework for governance and built on cloud ML platforms across AWS, Azure and GCP.
What's Included
What You Receive
Frequently Asked Questions
MLOps extends DevOps to handle what is unique about machine learning, where you version data and models as well as code, the same code can behave differently as data changes, and models degrade over time through drift. It adds model registries, retraining pipelines and drift monitoring on top of standard CI/CD.
Yes. Production AI needs more than a deployment pipeline. We align practices with the NIST AI Risk Management Framework, add controls for data and model security, monitoring for drift and misuse, and audit trails, so your AI is not just running but running responsibly and defensibly.