Machine Learning

Top 5 New SensiML Dataset Management Features

Boost Your Productivity with These New DCL Features
November 10, 2022

We’ve updated SensiML Analytics Toolkit with a variety of new features to make it easier than ever before for users to create and manage their AI/ML projects and visualize their datasets in insightful new ways.

Read more

SensiML @ Silicon Labs Works With 2022

Accelerating AI/ML for the IoT
September 16, 2022

Watch the video of Chris Rogers’ conversation with Ross Sabolcik, VP/GM of Industrial and Commercial IoT products at Silicon Labs, about the benefits of AI at the edge, key use cases, and how Silicon Labs and SensiML are working together to advance IoT smart sensing with ML.

Read more

Creating a Smarthome Device That Is Truly Smart

Building an Acoustic Aware Door Lock Using Silicon Labs' AI Accelerated xG24 Dev Kit
April 26, 2022

Part 1: Using Silicon Labs xG24 Dev Kit and SensiML Analytics Toolkit, we’ll transform a connected door lock into an innovative enhanced security smart lock.

Read more

A Little Bit About TinyML

How does SensiML fit into the picture?
March 16, 2022

We often get asked about TinyML – what it is, how it works, how we at SensiML fit into the picture, and for some real-world examples of how it can it useful. Let’s start with the definition. For this we turn to the authoritative body, the tinyML Foundation (tinyml.org).  Here’s what they have to say: “Tiny

Read more

New SensiML Analytics Studio Makes AutoML for IoT Edge Easier Than Ever

April 13, 2020

This week SensiML is releasing an all new version of its industry leading AutoML application SensiML Analytics Studio for building optimized embedded sensor algorithms for IoT devices.  SensiML Analytics Studio has existed since our inception as a core component of our AI Toolkit.  Previously it consisted of both a Python language interface for data scientists

Read more

The Case for AI at the Edge

February 21, 2020

Artificial Intelligence (AI) is becoming increasingly commonplace as organizations seek to bring effective decision making and operational efficiencies to business in ways that transform how humans and machines work together. As this transformation takes shape, the advantages of edge-based AI implementations over centralized or cloud-based models is changing how AI tools are being deployed and

Read more