arckit-mlops

An MLOps strategy defines processes for model lifecycle management, training pipelines, serving, monitoring, and governance. It ensures machine learning models are reliably developed, deployed, and retired throughout their lifecycle.

Model governance integrates compliance requirements and quality checks into the ML lifecycle to ensure responsible AI. It defines infrastructure and processes so AI systems adhere to standards like the UK Government AI Playbook and MOD JSP 936.

Monitoring machine learning models for drift requires defining continuous monitoring processes within your MLOps strategy. It involves setting up infrastructure and quality checks to detect when production data diverges from training data.

Yes, the MLOps strategy generator supports compliance with the UK Government AI Playbook. It specifically addresses these compliance requirements alongside MOD JSP 936 to ensure responsible AI deployment and model governance.

Yes, you can generate a deployment strategy for a computer vision model using Azure ML. The strategy details the training pipeline, deployment infrastructure, continuous drift monitoring, and adherence to responsible AI principles.

Defining machine learning operations maturity requires project context, requirements, and data models. These components are utilized to define infrastructure, processes, and quality checks for AI systems.