Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

5. 
Model Training
The model pipelines are always "offline", and its schedule will vary from
a matter of few hours to just one run per day, based entirely on the
complexity of the application. The training can also be initiated by time and
event, and not just by the system schedulers.
It includes many libraries of machine learning algorithms such as "linear
regression, ARIMA, k-means, decision trees" and many more, which are
designed to make provisions for rapid production of new model types as
well as making the models interchangeable. Containment is also important
for the integration of "third-Party APIs" using the "facade pattern" (at this
stage the "Python Jupyter notebook" can also be called).
You have several choices for "parallelization":
A specialized pipeline for individual models tends to be the
easiest method, which means all the models can be operated at
the same time.
Another approach would be to duplicate the training dataset, i.e.
the dataset can be divided and each data set will contain a replica
of the model. This approach is favored for the models
that require all fields of an instance for performing the
computations, for example, "LDA", 'MF".
Another approach can be to parallelize the entire model,
meaning the model can be separated and every partition can be
responsible for the maintenance of a fraction of the variables.

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This approach is best suited for linear machine learning models
like "Linear Regression", "Support Vector Machine".
Lastly, a hybrid strategy could also be utilized by leveraging a
combination of one or more of the approaches mentioned above.
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It is important to implement train the model while taking error tolerance
into consideration. The data checkpoints and failures on training partitions
must also be taken into account, for example, if every partition fails to owe
to some transient problem like timeout, then every partition could be trained
again.