Now that they have learned about all the algorithms which are present in supervised learning, we will
discuss about pipeline generation Pipeline basically helps us to create a complete flow from the region
to testing of data so that we can do this without writing the piece of code again and again.
So a machine learning pipeline is used to help automate machine learning flows, they operate by enabling
a sequence of data to be transformed and correlated together in a model that can be tested and evaluated
to achieve an outcome, whether positive or negative.
It helps us in saving more and more.
This can be loaded from a file using a biplane, and it also helps business applications to plug and
connect the pipeline directly.
So for this, let us have a look at the pipeline structure.
So basically a pipeline will help us to automate the Indian machine.
Learning will flow.
And using a pipeline, we can simply create a pipeline, begin to create the entire flow, how the details
should be going to the transformation phase.
And we will see the modalities so that next time when we want to use the model, we can simply pick
the fight from the pipeline and use the pipe itself and simply get the output from it.
We don't have to write the codes again and again.
So this is the pipeline.
So in the pipeline, we have the training data which goes into this complete pipeline, which we have,
this is the entire pipeline.
So in the pipeline for the day that will be cleaned, then it will be normalized.
Then we will impute the value.
Then feature engineering will be done after that, we can apply any of these or any of the model which
we want to apply in, for example, random photos or decision three, any type of model we can delete
here.
And once we have generated the model and despite this computer, we can simply put the testing data
inside the pipeline and make the prediction so we don't have to write this code again and again or train
the model again and again.
We can simply pick this pipeline and place it anywhere and use it so we will learn about how we can
implement in the next session and see how this can actually be implemented.