Scalable MLOps for AI Model Deployment & Monitoring

Scalable MLOps for AI Model Deployment & Monitoring

Application: Automating AI model deployment and lifecycle management for enterprise solutions.

Overview

Designed and implemented a comprehensive MLOps pipeline that streamlines the entire machine learning lifecycle from development to production deployment, ensuring reliability, scalability, and continuous improvement of AI systems.

Key Components

Infrastructure & Deployment

• Containerization: Built with Docker for consistent deployment across environments
• Model Versioning: Implemented MLflow for comprehensive model tracking and version control
• CI/CD Integration: Automated deployment pipelines with rollback capabilities

Model Evaluation & Monitoring

• AI Evaluation Framework: Built using Guardrails & Geval for real-time assessment of:
  • Hallucination rates
  • Reasoning accuracy
  • Model robustness
• Performance Monitoring: Automated monitoring with anomaly detection and alerting systems

LLM Integration

• Integrated LLMs into production workflows via Azure AI Services
• Automated data-driven decision-making processes
• Implemented fallback mechanisms for high availability

Results

• Reduced model deployment time from days to hours
• Improved system reliability with 99.9% uptime
• Enabled automatic model retraining based on performance metrics
• Streamlined collaboration between data scientists and engineers

Technologies Used

• MLOps Tools: MLflow, Docker, Kubernetes
• Evaluation: Guardrails, Geval
• Cloud Services: Azure AI Services, Azure ML
• Monitoring: Prometheus, Grafana
• Languages: Python, YAML