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How teams adopt nRF Cloud

nRF Cloud is not a tool you have to adopt all at once. Teams typically grow into it, turning on capabilities as their product matures and their needs change. Understanding this adoption arc helps you see where you might start and where the value compounds.

A typical adoption journey

  1. Debug. Many teams focus first on debugging data to catch crashes and see coredumps from real devices, often while still in development. The debugging view pays off immediately because it removes the need to reproduce faults by hand.
  2. Measure. Metrics such as memory usage, battery drain, connectivity errors, and uptime help with issue resolution too, but are more broadly important for characterizing fleet performance. When aggregated, these measurements can provide a baseline measurement of key performance indicators (KPIs), such as firmware stability, to be used later on when teams ship new features and fixes.
  3. Update. With metric collection in place, teams can ship updates over the air to their field testing units or beta fleet with confidence, knowing they will get feedback on how the release is performing relative to the captured baseline. When a product launch date arrives, they will already have practice inspecting release stability, and can now use groups and staged rollouts to de-risk new releases.
  4. Monitor. To be proactive about catching issues before they affect users, teams set alerts on metrics hitting certain thresholds, monitor new issues, and upload SBOMs for continuous vulnerability scanning. As a result, they use the platform primarily as an early-warning system rather than a forensic one.

Who uses nRF Cloud

There are various groups that typically get value from nRF Cloud:

  • Firmware and embedded engineers: debug crashes, inspect device performance, and drill down into logs.
  • QA and release managers: monitor test cohorts for issues and manage releases to production.
  • Support teams: check device state (battery level, errors reported) during triage, and link directly to a misbehaving device when escalating a ticket to an engineering team.
  • Product managers and architects: use fleet-wide trends to make decisions about reliability and roadmap. Depending on team make-up, product architects may also monitor CVE scanning status for newly reported vulnerabilities affecting fielded devices.

The nRF Cloud sandbox demo

The sandbox demo will be used in this course as part of an optional hands-on component to get familiar with the nRF Cloud interface. If you would like to get hands-on with some demo devices, navigate to the Overview page of the Shapemate project. Shapemate is a Bluetooth LE, battery-powered fitness wearable made by Acme Inc.

To start, inspect the first few charts showing device volume and software version trends:

Overview dashboard of the Shapemate fleet in nRF Cloud

You will notice that this is a fairly new product that has only started reporting data to nRF Cloud in the last few weeks. In the coming lessons, you will get the opportunity to dive deeper into this fleet and the initial stages of its journey.

What this course does not cover

nRF Cloud includes additional capabilities that fall outside the scope of this course. In particular, Location Services (fast, low-power device location using GNSS, cellular, and Wi-Fi; Assisted and Predicted GPS (AGPS + DGPS) and Assisted-GNSS), Message Routing Service (forwarding device messages to your own cloud and integrations), and Device Identity (provisioning via cryptographic identity) are not covered here. These are all documented on the nRF Cloud documentation page, and may be the subject of future courses.

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