Where to go next?

This guide has walked you through the Hello World example of edge sensor detection. The purpose of the Free Trial is to show you the tools available in the SensiML Toolkit for when you want to build your own applications. There are many features that are disabled in the free trial that will be unlocked once you upgrade your subscription.

Upgrade Your Subscription

The free trial version of the software is intended to give you a taste of what the SensiML Toolkit has to offer, but it has a lot of features disabled. See our subscription tiers and contact us to upgrade your subscription today.

Advanced Model Building

For data scientists we recommend looking into more of the advanced features within the SensiML Toolkit. In this guide we discussed the graphical interface using Analytics Studio, however, through Analytics Studio Notebook you can specify the features, transforms, training algorithm, and validation methods that go into your model. You can also specify different sampling, feature selection, and data augmentation techniques. To see how this works see the Advanced Model Building tutorial.

Data Depot

We provide a variety of more advanced projects outside of this guide which you can find on the Data Depot. Here you will find full curated datasets for industrial predictive maintenance, sports wearables, and more. These projects are meant to provide examples to help you get started with your own projects. We are constantly adding new datasets and welcome contributions.

Model Rehydration

Using Analytics Studio Notebook you can rehydrate a model and see the code/algorithms in a model that was generated using SensiML AutoML.

  1. Copy the Knowledge Pack UUID from the Explore Model page inside the Model Summary tab

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  1. Run the following commands in Analytics Studio Notebook to rehydrate the model

from sensiml import SensiML
dsk = SensiML()
dsk.project = '<Project Name>'
dsk.pipeline = '<Pipeline Name>'

kp = dsk.get_knowledgepack('<Knowledge Pack UUID>')
dsk.pipeline.rehydrate(kp)

#Replace <Project Name>, <Pipeline Name>, and <Knowledge Pack UUID> with your own project parameters

This will print out the model code. From here you can modify the code and re-train your model hyperparameters to improve the accuracy. For more information on the programmatic approach to model building see our Advanced Model Building tutorials for the Analytics Studio.

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