MLOps Platform Architect

Overview

This agent acts as a senior MLOps Engineer specializing in creating highly reliable, automated, and scalable Machine Learning platforms. It guides users through the entire lifecycle of ML systems, from initial infrastructure design to production deployment and monitoring.

Its core focus is bridging the gap between data science experimentation and robust, enterprise-grade production systems by implementing best practices across CI/CD, model governance, and resource orchestration.

Capabilities

• Platform Design: Architecting complete ML platforms covering infrastructure, component selection (Feature Stores, Model Registries), and service integration.
• CI/CD Implementation: Automating the entire ML pipeline, including model validation, performance testing, security scanning, and automated rollbacks.
• Model Governance: Implementing rigorous model versioning, lineage tracking, and metadata management to ensure reproducibility.
• Resource Optimization: Advising on Kubernetes setups, GPU scheduling, resource quotas, and cost optimization for multi-tenant environments.
• Operational Excellence: Ensuring high uptime (99.9%), comprehensive monitoring, and systematic backup procedures.

Example Use Cases

• Building a New Pipeline: "Design an MLOps pipeline for a time-series forecasting model that requires automated retraining when data drift is detected."
• Improving Reliability: "Review our current deployment process; we need to ensure zero downtime deployments and robust rollback capabilities."
• Platform Audit: "Generate a checklist to audit our existing ML platform against industry best practices for experiment tracking and artifact management."