Data Collection & Data Labeling Services
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Poor data collection causes machine learning projects to fail...and worse, it causes companies to lose faith in machine learning.
We’ve seen it over and over again. Project teams collect data for their first machine learning projects without fully understanding or thinking through the implications for model construction, training and validation. As a result, they don’t get the results they want, don’t get the results they could have gotten, and skeptics of new technology look like they won the day.
Reality AI Data Collection Services
Here are some common data collection mistakes and how to avoid them:
1 - Gaps in Coverage
Machine learning is an exercise in overcoming variation in targets and backgrounds with data. Collect data without thinking about sources of variation and different confounding variables, and your machine learning project will fail before you build your first model. For more on this, see our blog Successful Data Collection for Machine Learning with Sensors.
2 - Poorly designed data collection protocol
Especially in the early stages of the project, you want to think very carefully about the sensors you will use and the specific outputs you will retain. Too often we have seen project teams discard the most useful parts of their data, and fail to keep track of metadata that could have provided crucial explanatory insight. For more on this, see our blog Rich Data, Poor Data: Getting the most out of sensors.
3 - Collecting too much, or collecting too little
Data collection is usually the most time consuming, most expensive part of the machine learning project. If you collect too little data, you have to go back and do it all over again. But collecting too much is very expensive, and budgets are always limited. That’s why we recommend an iterative approach to data collection, where each stage is carefully planned to provide answers to a specific set of technical questions, increasing in difficulty with each round as earlier stages justify the investment. For more on this, see our blog 5 Tips for Collecting Machine Learning Data from High-Sample Rate Sensors.
4 - Labeling problems
Labeling is easily one of the most difficult challenges for most sensor-based machine learning projects. Human subjects are often unreliable, and data may not always be available from machines to support exhaustively labeled data. But resist the temptation to look for magical “unsupervised learning” techniques. These tend to take much more data and much more effort to produce inferior results than some creative thinking about the labeling challenge would have in the first place. At Reality AI, we are experts in designing ways of bootstrapping labels for intractable labeling challenges. See our forthcoming blog on this topic appearing soon.
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