Enterprise AI platform: GPU model training, MLOps lifecycle, optimized inference, managed data platforms, feature stores, and end-to-end ML workflow automation.
AI Platform
Complete ML lifecycle management from data preparation to production monitoring.
Distributed GPU training with mixed precision, gradient accumulation, and hyperparameter optimization.
Experiment tracking, model registry, A/B testing, and automated retraining pipelines.
TensorRT, ONNX Runtime, and custom kernels for low-latency production serving.
Lakehouse architecture with Delta Lake, data versioning, and governance controls.
Centralized feature engineering with point-in-time correctness and online/offline serving.
Drift detection, performance degradation alerts, and explainability dashboards.
ML Lifecycle
Defect detection, predictive maintenance, and process optimization models.
Drug discovery, genomics, and medical imaging AI.
Demand forecasting, grid optimization, and asset health models.
Notebooks, Spark, and analytics for model development.
From intent to verified engineering artifact.