Transforming experimental notebooks into production-grade intelligence using Databricks Lakehouse architecture.
Designing unified feature stores that provide AI-ready datasets. We bridge the gap between data engineering and modeling by ensuring high-performance features are available for training and inference.
Automating the experimentation journey. We implement MLflow patterns for experiment tracking, model versioning, and transition management to ensure every model is reproducible.
Scaling intelligence across the organization. From Real-Time Model Serving to High-Throughput Batch Inference, we ensure your models are resilient, monitored, and performant.
Graph-based Route Optimization (Spark MLlib).
Last-mile routing updates based on real-time port congestion data.
Automated MLflow retraining loops ensure routing models never drift during peak logistics seasons.
Computer Vision (CNN) for Structural Fatigue Detection.
Prioritizing repair crews for wind turbine assets in remote regions.
Managed lifecycle for computer vision models, from edge device inference to centralized tuning.
Deep Learning Model Serving (Torch/PySpark).
High-throughput image prioritization for diagnostic triage.
Ensures sub-second latency for medical image serving while maintaining 99.9% reliability.