Transformation blocks take raw data from your organizational datasets and convert the data into files that can be loaded in an Edge Impulse project. You can use transformation blocks to only include certain parts of individual data files, calculate long-running features like a running mean or derivatives, or efficiently generate features with different window lengths. Transformation blocks can be written in any language, and run on the Edge Impulse infrastructure.
Transformation blocks can output data to either:
- A project - here the data in your dataset is placed in an Edge Impulse project, and you'll have all the normal features from the studio available to build your machine learning models. This is great if you already have an idea what you want with the data, and are looking for a reproducible pipeline.
- Back in the dataset - here data is placed back in the dataset. This is great for extracting long running features, batch jobs, or combining data from multiple sources - even when you don't want to place the data in a project yet.
For both of these you can find tutorials here:
Updated 2 months ago