- Latency - Machine-to-machine or other on-premise use cases may have latency requirements that cannot be met with inference in the cloud.
- Data volume - If many sensors need to be analyzed by the ML model the shear data volume may make cloud inference impractical and/or too costly.
Collect and adapt data, deliver results
- First of all you need to get hold of the data, from machines or other data sources, possibly using different protocols and data formats.
- Then, you need to prepare the data. In a live environment you get streaming data from each sensor independently, while the ML models expects samples with data from each sensor in a specific order. To solve this you need some way to time-align the live streams and possibly re-order the data. You may also have sensors that delivers data with different frequencies, so that data from slower sensors must be repeated or interpolated to fill up the samples expected by the ML model.
- Finally, you need to deliver the results of the ML inference, whether it’s a trigger that should be sent to some onsite machine or system, or some insights that should be delivered to an IT system, locally or in the cloud.
Manage models and code
- An ML model file, exported from the toolchain used to train the model
- An execution environment. In most cases this is the same framework used for training, or a subset thereof, like Tensorflow, Scikit-learn or Pytorch. Model exchange formats like ONNX will open up for generic execution environments.
- Code to initiate the ML framework and load the model file.
Test your models with real data
Key requirements that the Crosser Platform addresses include:
- Visualize and store all models available for deployment
- Ways to include the ML models in a context without having to write all code for collecting, preparing and acting on data
- A runtime environment in the edge that is easily managed
- Orchestration and mass deployment of ML models and processing flows
- Version control making updates simple, secure and managed
Start Innovating Today
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Sign up for a free trial account. Design and deploy your first Flows today.
- Sign up
- Login and start designing your data flows
- Run the flow in Crosser test environment or download local test node
- Test with real data