Edge Impulse is the leading development platform for Embedded Machine Learning, used by over 1,000 enterprises across 10,000 Machine Learning projects worldwide, and helping developers leverage the power of Machine Learning. 

We start the episode by discussing what Embedded Machine Learning means for IoT and connected devices, lensing in on why people may be sceptical to deploy Machine Learning on an MCU. We then explore key use cases around ML, core differences between Embedded ML and ML-based server projects and the main challenges when it comes to EML deployment. We finish the episode analysing the role of signal processing in EML and where we can go for the future with EML!

Sit back, relax, tune in and discover:

⚙️  Background in Embedded Engineering and IoT  00:30 – 04:26

⚙️  Growth for companies among the pandemic 04:26 – 05:28

⚙️  Why do people tend to be sceptical about Embedded Machine Learning? 05:28- 08:19

⚙️  What are the use cases around embedded machine learning and why is it important? 08:19- 10:53

⚙️  What are the core differences between ML-based server projects and embedded machine learning? 10:53- 12:17

⚙️  The main challenges in developing Embedded Machine Learning solutions and algorithms 12:17- 14:53

⚙️   Why has embedded ML not been as widely used before? 14:53- 17:55

⚙️   How do signal processing and Embedded ML work together 17:55- 20:22

⚙️  Where can Embedded Machine Learning go in the next five years, how will this affect people in the everyday life? 20:22-23:00

⚙️  Future use cases of ML: Formula 1 Cars 23:00-24:28

⚙️  Security and Machine Learning- What effects does security risk have on Machine Learning? 24:28- 31:15

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