Embedded/edge ML folks: what actually eats the most time ,getting data, or cleaning/labeling it (time series sensor data, not computer vision/audio)? [D]
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Discover the biggest time-consuming task in edge ML projects with time-series sensor data
Action Steps
- Collect and analyze data from various sensor sources to identify patterns and challenges
- Implement data cleaning and labeling techniques to improve data quality
- Develop and train ML models using the collected data
- Optimize and deploy the models on microcontrollers
- Monitor and evaluate the performance of the deployed models
Who Needs to Know This
Data scientists and ML engineers working on edge ML projects with sensor data can benefit from understanding where time is spent, to optimize their workflow and improve productivity
Key Insight
💡 Data cleaning and labeling are often the most time-consuming tasks in edge ML projects with time-series sensor data
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🤖 Edge ML with sensor data: what eats the most time? Data collection, cleaning, or model deployment? 📊
Key Takeaways
Discover the biggest time-consuming task in edge ML projects with time-series sensor data
Full Article
I'm trying to understand where people doing sensor based ML on microcontrollers (IMU, accelerometer, vibration ,that kind of time-series data) actually lose the most time. When you've built something like this, what was the bottleneck: Getting enough real world data in the first place? Cleaning / labeling / organizing the data you have? Actually building and training the model? Getting it optimized and deployed on t
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