MLOps & AI Lifecycle Management

Overview

MLOps & AI Lifecycle Management provides end-to-end oversight of model development and deployment. By automating testing, integration, and monitoring, organizations can maintain high-performing models and adapt quickly to changing data or requirements.

Key Capabilities

• Automated Model Testing & Version Control
• CI/CD Pipelines for AI Solutions
• Model Monitoring, Drift Detection & Alerts
• Infrastructure as Code (Docker, Kubernetes, etc.)

Approach

• Phase 1: Data Assessment (DAS)
  Evaluate your data environment and plan how models will be refreshed, retrained, or replaced. Identify infrastructure requirements for automated processes.

• Phase 2: Proof of Concept (PoC)
  Implement a pilot CI/CD pipeline for at least one AI model. Validate continuous integration, deployment, and monitoring workflows.

• Phase 3: Production (Prod)
  Roll out full-scale pipelines and integrate models with production systems. Ensure real-time performance tracking, resource scaling, and robust model governance.

• Optional CI/CD
  By design, MLOps is CI/CD-centric. We provide ongoing maintenance and on-call support to keep pipelines, models, and infrastructure running at peak efficiency.

Example Use Cases

• Realtime Model Updates: Automated deployment of new model versions, or updated tables.
• Scheduled Retraining: Ongoing pipeline refreshing models on updated datasets.
• Scalable Infrastructure: Leveraging Kubernetes or Docker for high-volume, low-latency automation & predictions.