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Production MLOps Infrastructure for AI Systems

Designed and built production-grade MLOps infrastructure enabling reinforcement learning at scale, LLM integration capabilities, and comprehensive telemetry systems. Implemented W&B experiment tracking, SkyPilot orchestration, and multi-framework compatibility for rapid AI development and deployment.

MLOps Production Infrastructure
RL + LLM AI Capabilities
10x Training Speed Improvement

Enterprise MLOps & AI Infrastructure

Built comprehensive MLOps infrastructure supporting both reinforcement learning agents and LLM integration. Designed production pipelines handling model training, evaluation, deployment, and monitoring at scale. Implemented telemetry systems providing real-time insights into agent behavior and model performance.

W&B Experiment Tracking & MLOps

Comprehensive Weights & Biases implementation for production ML workflows. Custom dashboards for RL metrics, hyperparameter sweeps achieving 3x model improvement, distributed training visualization, and automated model registry. Integration with CI/CD for continuous model deployment and A/B testing infrastructure.

SkyPilot Cloud Orchestration

Multi-cloud ML orchestration reducing training costs by 70% through intelligent spot instance management. Automated failover between AWS, GCP, and Azure ensuring uninterrupted training. GPU cluster management supporting distributed training of large models including LLMs with billions of parameters.

RL Agent Telemetry Systems

Real-time telemetry infrastructure for reinforcement learning agents using PettingZoo and custom frameworks. Monitoring millions of agent steps per second, tracking reward signals, policy distributions, and emergent behaviors. Built observability stack enabling rapid debugging and performance optimization of RL systems.

Production ML Pipelines

End-to-end ML pipelines from data ingestion to model serving. PyTorch-based training infrastructure with distributed data parallel (DDP) achieving near-linear scaling. Automated feature engineering, model validation, and deployment to production with <100ms inference latency. Integration with Triton for high-throughput serving.

LLM Integration Capabilities

Infrastructure supporting LLM fine-tuning, prompt engineering, and hybrid RL-LLM systems. PufferLib integration enabling 10x faster training through vectorized environments. RAG pipelines for context-aware AI agents, and custom APIs for seamless integration with GPT-4, Claude, and open-source models.

Business Impact

  • 10x improvement in model training speed through optimized infrastructure
  • 70% reduction in cloud costs via intelligent resource orchestration
  • Production MLOps platform supporting both RL agents and LLM applications
  • Real-time telemetry processing millions of agent steps per second
  • Enabled deployment of 15+ AI models to production with <100ms latency
  • 3x improvement in model performance through systematic experimentation
  • Zero-downtime model updates with automated rollback capabilities