Transparent machine-learning pricing platform for actuaries.
AI SKILLS · Hardware & electronics
Deploy and check ML on embedded devices
Train, compress and deploy small models to microcontrollers, and measure accuracy, latency and power on the device.
Why this skill matters
Edge models lose accuracy when quantised and behave differently on real sensor data than in training.
What to learn
- Collect and label representative sensor data
- Quantise a model and measure on-device accuracy and latency
- Plan monitoring and updates in the field
Before you begin
Basic Python and embedded development
PUT IT INTO PRACTICE
Build a work sample
Build a keyword-spotting or vibration model, deploy it to a dev board, and compare on-device results with the training metrics.
Evidence of progress
A short report with on-device accuracy, latency, memory use and known failure cases.
This exercise is an original ToolGrail suggestion. Use public, synthetic, or approved data.
LEARNING RESOURCE
Edge Impulse documentation
Edge Impulse · Resource checked 2026-11-12
Open learning resource ↗This may be documentation, a framework, or a training path. Product access, accounts, or paid licenses may be needed for hands-on exercises.
Tools you could practice with
Matched by related tasks. Use a tool you already have access to; these are examples, not course requirements.
AI coding agent for C and C++ that runs sanitizers, the debugger and tests before showing a diff; page-cited embedded docs.
Query your data in plain English or SQL, build datasets, and fit Bayesian models.
Collaborative data notebook with an AI agent for analysis.
Skill descriptions, exercises, and priorities are editorial. They are not professional qualifications or guarantees of employment. Read our skills methodology →
Find the right AI tools for any job, task or industry.