Next steps for TinyML development
In the scope of this book, we only discussed how to run inference on ESP32 by using different TinyML frameworks. However, in real-world scenarios, we need to do more. Let’s review the ML development stages once more and have a short discussion of them in terms of the engineering work needed:
- Project requirements: A project starts with a need and requirements that list what to do in response to that need. A machine learning project is no exception for that. The requirements of an ML project usually reveal a lot about the nature of data in the project. With a requirement analysis, we can understand what data we need to collect, the sources of data, how we can collect it, any option to import external data, data versioning requirements, etc. In addition, a requirements document can have information about the performance of the output model, such as the accuracy, response time, and memory limitations. Project requirements have a direct...
