Enterprise MLOps Platform & GPU Cluster Management
End-to-end machine learning infrastructure supporting distributed training, automated model deployment, and production-scale inference serving across GPU clusters.
Architecting a scalable ML platform capable of training large language models, serving 1M+ daily inferences, and managing distributed GPU workloads with efficient resource utilization.
Distributed training infrastructure with automatic hyperparameter tuning and experiment tracking across GPU clusters.
High-performance inference serving with dynamic batching, model versioning, and A/B testing capabilities.
End-to-end MLOps pipeline with automated model deployment, drift detection, and performance monitoring.
Provisioned multi-node GPU clusters, configured CUDA environments, and established high-speed networking for distributed training.
Built scalable data ingestion pipelines, implemented distributed training workflows, and established experiment tracking systems.
Deployed production inference servers with auto-scaling, model versioning, and real-time monitoring capabilities.
Implemented comprehensive MLOps workflows, model drift detection, and automated retraining pipelines for production-scale deployment.
Successfully deployed an enterprise-scale ML platform that supports training of large language models with billions of parameters while serving millions of daily inferences with sub