SensiML Analytics Toolkit

Collect vibration and acoustic data from industrial equipment using sensors.

Label data to identify normal and abnormal operating conditions.

Use SensiML AutoML to generate a predictive maintenance model.

Deploy the model to an edge device on the equipment.

Continuously monitor equipment and trigger alerts when anomalies are detected.

Collect accelerometer and gyroscope data from wearable devices.

Label data with different hand gestures (e.g., swipe, tap, rotate).

Use SensiML AutoML to train a gesture recognition model.

Deploy the model to the wearable device.

Recognize gestures in real-time and trigger corresponding actions.

Collect audio data from microphones in smart home devices.

Label data to differentiate between speech and background noise.

Use SensiML AutoML to create a voice activity detection model.

Deploy the model to the smart home device.

Detect voice activity and activate voice-controlled features only when needed.

Collect data from soil moisture sensors, temperature sensors, and weather stations.

Label data to identify normal and abnormal crop growth patterns.

Use SensiML AutoML to train an anomaly detection model.

Deploy the model to an edge device in the field.

Monitor crop health and environmental conditions, and trigger alerts for anomalies.

Collect data from vibration sensors, pressure sensors, and flow meters.

Label data to identify normal and abnormal operating conditions.

Use SensiML AutoML to create an equipment condition monitoring model.

Deploy the model to an edge device at the equipment site.

Continuously monitor equipment and provide real-time insights into its condition.

Collect data from strain gauges, accelerometers, and other sensors embedded in infrastructure.

Label data to identify normal and abnormal stress patterns.

Use SensiML AutoML to train a structural health monitoring model.

Deploy the model to an edge device on site.

Continuously monitor infrastructure and trigger alerts when critical thresholds are exceeded.