Take machine learning from Jupyter notebooks to resilient production infrastructure.
Automated model retraining, feature stores, drift monitoring, and edge model quantization.
Typical Enterprise Bottleneck vs. The WEBTRIP Solution
Legacy Client Bottleneck
Data science teams build models that stay stuck in local notebooks, drift over time, and crash under production traffic.
The WEBTRIP Architecture
We deploy automated training pipelines (Kubeflow/MLflow), model versioning, real-time drift detectors, and quantized ONNX runtimes.
Reference Architecture: MLOps
Field Inference Logs
Drift Detection Stream
Automated Retrain
MLflow / GPU Worker Pool
Quantized Deployment
Over-the-Air Model Update
Capabilities & Deliverables Matrix
Automated Training Pipelines
Triggering retrains automatically when statistical feature drift is detected.
Model Quantization & Edge Compilation
INT8 quantization and TensorRT compilation for low-power edge gateways.
Model Observability & Telemetry
Tracking inference latency, input drift, and confidence scores in real-time.
Ready to Build or Modernize Your Software Infrastructure?
Schedule a 30-minute technical feasibility call with our senior solutions architects to explore custom MLOps systems.