Unlike most general purpose AI frameworks, SensiML Analytics Toolkit includes a client application to make the process of collecting, labeling, and cleansing supervised ML datasets vastly easier, manageable, and scalable from most methods currently in use by developers. In fact, most developers faced with dataset creation and management resort to their own means as the
Community Edition is best suited for those users who are still exploring what edge AI can do for them and how they might apply it to their IoT application(s). By working through provided example applications, it is possible to familiarize yourself with what can be done and start formulating a defined project. We also have
SensiML makes sample datasets available for evaluating the toolkit even prior to having your own data. We publish a growing collection of additional datasets for evaluation in our application examples. All tiers of SensiML from Community Edition through Enterprise edition can utilize these sample datasets. The SensiML Data Capture Lab tool also greatly simplifies the
SensiML Analytics Toolkit is offered on both a monthly and annual license SaaS service plan with service levels that fit the needs and project phases of developers, engineers, and data scientists building intelligence into their products. Trial Edition – A zero-cost entry to our software enabling software evaluation and training on the SensiML edge IoT
For those familiar with Python ML programming, we make the full capabilities of SensiML’s client API available in our Python SDK for greatest flexibility. More details can be found at https://pypi.org/project/SensiML/.
If you are developing a smart IoT device or embedded application that involves sensors and sensor data processing, it is very likely that you can benefit from the SensiML Analytics Toolkit. The graphic below provides just a few examples of use cases where SensiML can be utilized to build models that transform raw sensor signals
SensiML welcomes those wish to publish datasets, subject to review, on our Data Depot (https://datadepot.https://sensiml.com). Such datasets can be submitted and made available for distribution to others under one of several licenses. See datadepot.https://sensiml.com for more information.
The SensiML TestApp is an application that shows real-time event classifications output from the SensiML AI model (Knowledge Pack) running on your edge device. The SensiML TestApp can show custom class names and pictures with your live classification results. TestApp is available as both a Windows10 PC application, or for field use, as an Android
SensiML Open Gateway is an easy-to-adapt connectivity tool that can be extended and modified quickly. Written in Python, SensiML Open Gateway can run on a variety of platforms and overcome communication limitations of small-form-factor IoT devices. Out of the box, the application can accommodate many different connection types and sensor configurations and with user customization,
Analytics Studio is the core AutoML application that runs in the cloud to build models based on your provided labeled datasets and return executable code for predictive models that can run on your target edge hardware platform. Designed as a UI based tool, SensiML Analytics Studio provides a standardized workflow for selecting labeled data and
General purpose AI frameworks were made to simplify the application of machine learning algorithms to datasets across a broad array of applications. Problems ranging from credit card fraud detection to image classification or even predicting survivors of the Titanic disaster from passenger metadata have been tackled using such tools. Moreover, these tools are typically cloud-centric
Depending on your perspective, the answer is both yes and no. By definition, machine learning is the principle of providing systems the ability to learn and improve from experience without being explicitly programmed. In place of explicit code or instructions, such devices are ‘taught’ through the use of labeled training data applied to classifier models
