Cloud Analytics
AutoML PipelineBETA
Automated machine learning from operational data to deployed edge model. Feature engineering, architecture search, A/B testing, and one-click deployment -- no data scientist required.
Pipeline Stages
Six automated stages from raw telemetry to production edge model.
Data Curation
Automatic selection of training windows from operational history. Outlier removal, class balancing, and feature normalization handled without manual intervention.
Feature Engineering
Time-domain statistics, frequency-domain features (FFT, wavelet), rolling windows, and cross-sensor correlations generated from raw telemetry.
Model Selection
Bayesian hyperparameter search across Isolation Forest, XGBoost, LSTM, and Autoencoder architectures. Best model selected by validation AUC and inference cost.
A/B Testing
Deploy candidate model alongside production baseline. Route 10% of inference traffic to the challenger. Promote automatically if precision improves by 2%+.
Edge Deployment
One-click ONNX export with INT8 quantization. Staged rollout to edge fleet with automatic rollback if prediction quality degrades.
Continuous Monitoring
Drift detection on input distributions and prediction confidence. Triggers automatic retraining when model accuracy drops below configured threshold.
Pipeline Specifications
| Training Data | Auto-curated from time-series telemetry |
| Feature Library | 120+ pre-built industrial features |
| Model Architectures | Isolation Forest, XGBoost, LSTM, Autoencoder, Random Forest |
| Hyperparameter Search | Bayesian optimization, 200 trials |
| Export Format | ONNX with INT8/FP16 quantization |
| A/B Test Duration | Configurable, default 7 days |
| Drift Detection | KL divergence + PSI on input features |
| Retraining Trigger | Accuracy drop > 3% or manual |
ML Models That Improve Themselves
Point the pipeline at your operational data and let it build, test, and deploy production-grade models to every edge device in your fleet.