Core Concepts
All You Need to Know
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All You Need to Know
Last updated
Was this helpful?
You do not need to be an expert in AI research to develop or deploy ML models. NatML focuses on making ML deployment as painless as possible for interactive media developers. Before jumping in, It is crucial to understand a few core concepts, and how they interact with one another:
An ML model is a 'black box' which consumes one or more input features and predicts one or more output features. For example, a vision classifier receives an image feature and produces a probability distribution feature, telling you how likely the image is one label or another.
At NatML, we prefer to refer to ML models as graphs. It's pedantic, sure, but avoids any ambiguity.
These are models that run predictions on the local device. They are exposed with the class.
NatML supports working with (.mlmodel
), (.onnx
), and (.tflite
) edge graphs.
A feature is any data that can be consumed or produced by an . For example, you will use a lot of image features when working with vision models. NatML has built-in support for common features that might be used with ML models, including Texture2D
and WebCamTexture
instances:
Predictors are lightweight primitives that use one or more models to make predictions on features. They are self-contained units that know how to transform inputs into a format that a model expects. But more importantly, they are able to transform outputs of a model into a usable format. For example, you might have a predictor that uses the MobileNet ML model to classify images:
Whereas a raw classification model outputs a probability distribution, the classification predictor can transform this distribution into a form which is much more usable by developers. It simply returns a class label (string
) along with a classification score (float
):
Every has a corresponding . This type describes the feature and data that is contained within it. Similarly, every has a set of input and output feature types, describing what data the model can consume and produce, respectively.
You can create custom predictors for different models, share them on !