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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

The SensiML Analytics Toolkit enables the swift development of smart IoT sensor devices while simplifying the complexities of data science. It focuses on creating compact algorithms designed to run on small IoT endpoints instead of relying on cloud processing. By gathering precise, traceable, and version-controlled datasets, it enhances data integrity. The toolkit employs advanced AutoML code generation to facilitate the rapid creation of autonomous device code. Users can select their preferred interface and level of AI expertise while maintaining full oversight of all algorithm components. It also supports the development of edge tuning models that adapt behavior based on incoming data over time. The SensiML Analytics Toolkit automates every step necessary for crafting optimized AI recognition code for IoT sensors. Utilizing an expanding library of sophisticated machine learning and AI algorithms, the overall workflow produces code capable of learning from new data, whether during development or after deployment. Moreover, non-invasive applications for rapid disease screening that intelligently classify multiple bio-sensing inputs serve as essential tools for aiding healthcare decision-making processes. This capability positions the toolkit as an invaluable resource in both tech and healthcare sectors.

Description

Sensing Feeling provides a comprehensive lineup of cutting-edge visual sensing solutions aimed at improving safety, efficiency, and risk management within various settings. The SensorMAX system seamlessly integrates with pre-existing CCTV and control frameworks to deliver real-time video analytics while ensuring that no images are recorded or transmitted, thereby maintaining user privacy. Additionally, the Visual Processing Engine (VPE) functions at the edge, interfacing with current Video Management Systems (VMS) or Network Video Recorders (NVR) to process multiple feeds concurrently. SensorMAX is equipped with a variety of algorithms suitable for tasks such as monitoring crowd density, detecting anomalies, and ensuring compliance with personal protective equipment (PPE), while it can also connect with SCADA systems to enable automated alerts and responses. On the other hand, SensorCODE is specifically designed for integration into low-power IoT edge devices, allowing for uninterrupted remote sensing and real-time telemetry across networks like WiFi, LoRaWAN, and 4G/5G, making it a versatile option for modern applications. This innovative technology ensures that users can respond to incidents swiftly and effectively, ultimately contributing to safer environments.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Docker No 

Integrations

Docker Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App Yes 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

SensiML

Founded

2017

Country

United States

Website

sensiml.com

Vendor Details

Company Name

Sensing Feeling

Founded

2016

Country

United Kingdom

Website

sensingfeeling.io

Product Features

Machine Learning

Deep Learning Yes 
ML Algorithm Library Yes 
Model Training Yes 
Natural Language Processing (NLP) No 
Predictive Modeling Yes 
Statistical / Mathematical Tools Yes 
Templates Yes 
Visualization Yes 

Alternatives

Alternatives