Data Engineering for the Edge

If the eFabric™ platform is the "Factory," then data is the high-octane fuel that powers it. In the domain of TinyML, the success of a model is determined less by the complexity of its layers and more by the integrity and diversity of its training data. Because edge models are intentionally "small" to fit on microwatt silicon, they lack the computational "headroom" to overcome poor data.

This section details the rigorous data engineering standards required to build production-grade models for the TML120 module.

🚀The 80/20 Rule

“Edge models do not have the 'intelligence' to ignore bad data. Spending 80% of your time on Data Engineering is the standard for successful TinyML deployment.”