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As part of the prerequisite of most machine
learning projects, we need a training dataset.
A training dataset is a
large group of labeled examples.
A machine learning system is going to learn patterns
inside the training dataset and store that knowledge
in something that is called a model.
This model is supposed to define as close
as possible the relationship between features and the
target label, in a common type of machine
learning method called supervised learning.
The way to create this kind of model is
based on analyzing a large group of labeled examples.
We are basically training our
model using the labeled examples.
Once we have trained our model with those
labeled examples, we can use that trained model
to predict the label on unlabeled examples.
When looking on the lifecycle of a model in
a machine learning system, we have two main phases.
The training phase or also called the
learning phase, which means creating the model.
The main idea is to utilize or
use some learning algorithms that will build
a model using the training dataset.
There are all kinds of learning algorithms
and we'll talk about them later.
After training a model, we would like to use
it in the real world to perform something useful.
So the next phase or the next
stage of a model is called inference.
In machine learning, inference means applying
the trained model in an actual
machine learning system working in a
production environment for making ongoing predictions.
It is also important to mention that this inference
represent a specific snapshot of the trained model.
In many practical machine learning use cases, the system will
keep training new and better models all the time using
new data, and in some time interval it will replace
an existing inference with a new one.
As a nice example, let's say we would like to develop
a mobile application that can recognize the type of animal from
a picture taking in real time by the end user. Okay.
So as part of the training phase, we will
have a very large dataset to train a
model like 1 million images of different animals.
This training process will be performed in
a dedicated, very expensive hardware setup located
in a centralized data center.
All the images used for training are located in
the same data center. After the training is done,
okay, now we are moving to the next stage.
We have a trained model that can be sent
as a snapshot to the end user mobile devices.
So when someone is taking a picture using
her or his phone, the local application on
the mobile device can immediately identify or classify
the type of animal on that picture.
The inference stage, which is the actual prediction
or classification, is done on the device level. Okay.
Another option is to perform the inference
stage in the centralized data center.
So the end user will take a picture.
The picture will be sent as raw data to the cloud. Okay.
And in the cloud there will be an application which
will use the latest model to perform the inference.
The result will be sent back to the end user device.
There are, of course all kind of prons
and cons like latency issue and so on.
Today, because of the end user devices that we
are using today are getting faster and better.
So there are more and more use cases where the
inference can be done on the end user device.
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