MLOps

We implement automated MLOps monitoring pipelines (using AWS SageMaker Model Monitor and MLflow) that continuously track data drift and concept drift in production. When model performance degrades below established thresholds, automated alerts trigger retraining workflows using real-time data.

A feature store acts as a centralized repository for storing, discovering, and sharing curated ML features across teams. It ensures consistency between training datasets and real-time inference models, eliminating duplicate feature engineering and preventing data leakage.

MLOps automates model packaging, containerization, unit testing, and shadow or canary deployments. This allows data science teams to safely push new ML model versions to production endpoints with zero downtime and instant rollback capabilities.

We establish end-to-end model governance by tracking dataset lineage, maintaining detailed model audit logs, enforcing encrypted storage, and securing REST API endpoints via role-based access control (RBAC) to ensure full compliance with regulatory standards.

While traditional MLOps focuses on tabular and predictive machine learning models, LLMOps specializes in managing Large Language Models. LLMOps handles foundation model fine-tuning, prompt engineering version control, vector database retrieval (RAG), context-length performance monitoring, and LLM output guardrails.

We deploy automated CloudOps solutions using AWS Lambda, IAM AssumeRole principles, and CloudFormation to continuously scan multi-account cloud environments. Coupled with real-time alerting systems (like Zabbix), this automates security health checks, detects CPU/memory anomalies early, and optimizes infrastructure costs.