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TensorFlow and Cloud
It starts with ingesting your data, followed by data validation, feature engineering,
training, and serving. Google has created libraries for each major phase of the
pipeline, and there are frameworks for a wide range of deployment targets. TFX
implements a series of ML pipeline components. All of this is made possible
by creating horizontal layers for things like pipeline storage, configuration, and
orchestration. These layers are very important for managing and optimizing
the pipelines and the applications that you run on them.
You will need to install it first. TensorFlow Extended can be installed using the pip
command:
pip install tfx
In the following section we will cover the fundamentals of TFX, its architecture,
and the various libraries available within it.
TFX Pipelines
The TFX pipeline consists of a sequence of components that implement an
ML pipeline, specifically, ensuring the scalability and high performance of the
underlined ML task. It includes modeling, training, inference, and deployment
to web or mobile targets. A TFX pipeline includes several components, with each
component consisting of three main elements: Driver, Executor, and and the
Publisher. The driver queries the metadata store and supplies the resultant metadata
to the executor, publisher accepts the results of the executor and saves then in
metadata. The executor is the one performing all the processing. As an ML software
developer, you will need to write code that runs in the executor depending upon the
component class you are working with:
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