is a subset of TensorFlow for deploying models to
smartphones and embedded Linux devices, like
the Raspberry Pi. It contains functions necessary
for inference, but it cannot be used for training.
You’ll also find TensorFlow Lite Micro within that
framework, which targets microcontrollers.
Other frameworks for developing machine
learning models include Sci-Kit Learn, Shogun,
PyTorch, CNTK, and MXNet. Apple has their own
proprietary system called Create ML that allows
you to train models and Core ML to deploy them
to macOS, iOS, watchOS, and tvOS.
Online editors like Google Colab provide a
development interface and pre-installed machine
learning packages, such as TensorFlow. While
they may not be ideal for production-level training
and deployment, they offer a fantastic learning
environment for working with machine learning,
especially for creating TinyML models.
Edge Impulse is a graphical online tool that
helps you analyze and extract features from
your data, train machine learning models,
and generate efficient libraries for performing
inference on microcontrollers (see “Teach a
Faux Nose New Tricks” on page 37). I use Edge
Impulse for my motion and audio classification
projects (Figure
F
).
Lobe.ai is another graphical online tool used
for training machine learning models, focused
on classifying images. It allows you to download
trained models in several formats, including
Core ML, TensorFlow, and TensorFlow Lite.
While these models might work on single-
board computers and smartphones (see “Trash
Classifier” on page 44), they would require further
optimization to function well on microcontrollers.
A FEW TOOLS HAVE
HELPED MAKE
TRAINING AND
DEPLOYING MACHINE
LEARNING MODELS
SIGNIFICANTLY
EASIER.
27
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