Distributed Machine Learning At The Edge


This architecture enables an iterative machine learning application that distributes its components across the cloud and the factory edge. It does this by distributing a CrateDB cluster across these two layers.

In the cloud layer, an application reads sensor data from the CrateDB cluster and trains a MLP model from the data, which it then publishes. It also runs a Prometheus node that triggers a retraining of the model if the model’s predictive quality degrades, as well as alerting users based on the predictions emanating from the prediction layer.

In the edge layer, an application uses the latest published MLP model and uses it in conjunction with a stream of data from the factory sensors. It publishes its predictions as well as model quality as a series of metrics to the Prometheus node running in the cloud layer.



This architecture makes use of the following components and CrateDB integrations:

  1. Ingestion of data from machine sensors to the CrateDB nodes at the factory edge.
  2. Distributed Deep-Learning with CrateDB and TensorFlow
  3. A stream of data from the factory sensors to the prediction layer application.
  4. Model quality and prediction metrics being pushed to Prometheus.
  5. Prometheus Alertmanager triggering a model rebuild if the model quality degrades.
  6. Prometheus Alertmanager triggering an alert to users if the prediction layer’s predictions fall within dangerous thresholds.