Take It to the Edge: Classification and Cloud Connectivity

Many times, we focus on performing machine learning locally on-device, with no need for internet connectivity. This reduces latency, simplifies projects, and can sometimes reduce costs. However, some projects do require connectivity to the cloud in order to set a status, update a dashboard, log an activity or event, or allow a user to interact with a device. This month we're highlighting two example projects that use Edge Impulse plus Blues Wireless to send data to the cloud.

Follow the steps to complete this activity and be entered into our giveaway to win December's prize bundle - a Blues Starter Kit and Edge Impulse Swag.

Join the Edge Impulse community at hackster.io/EdgeImpulse
Sign up for an Edge Impulse account at studio.edgeimpulse.com/signup
Review resources below.
- Resource #1: Appliances Identifier Smart Energy Meter Project
- Resource #2: Running Faucent Alert System Project
Complete the quiz below to show us what you learned!

Submissions close January 1st at 11:59PM Pacific.

Name

First NameLast Name

Email

example@example.com

Hackster Profile URL Link

Please include the entire URL link to your Hackster Profile: e.g. https://hackster.io/user-name

Email Used to Sign Up to Edge Impulse

Please include the email address you used to create an Edge Impulse account.

Country

Street Address

Street Address Line 2

1. In machine learning projects, using Transfer learning allows you to:

2. A Mel Filterbank Energy (MFE) block is great for processing what type of machine learning data?

3. Once the Edge Impulse CLI is installed, this command will launch the service to login and connect a Raspberry Pi to the Studio:

4. In the Edge Impulse Studio, the Data Acquisition screen is where you can:

5. This service from Blues Wireless can receive data from a Notecard and route data to cloud applications and services.

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