Bridging the gap between the Jupyter Notebook and the production server. We build automated CI/CD for models, robust registries, and highly-optimized inference infrastructure.
Version-controlled data, reproducible training pipelines, and automated testing to ensure new models are deployed safely and consistently.
Centralized hubs to track ML models, their hyperparameters, metrics, and deployment statuses across testing, staging, and production environments.
Low-latency serving architectures using technologies like TensorRT, ONNX, and vLLM to maximize throughput and minimize cloud compute costs.
Safely rollout new model versions by routing a fraction of live traffic, ensuring performance metrics hold up in the real world before full substitution.
Work with engineers who know how to containerize, scale, and maintain ML pipelines in Kubernetes and modern cloud infrastructure.
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