Build a Pipeline: Automate Your Machine Learning Workflow

Pipe connected to wall outside

In machine learning, we have the ability to automate our workflow. In this article, I’m going to help you get started at a beginner’s level on building the pipeline in ML with Python.

What Is the Pipeline?

A machine learning pipeline is used to automate our machine learning workflows. The way they work is by allowing a sequence of data to be transformed and correlated together in a model that can be tested and evaluated to achieve an outcome, whether it is positive or negative.

We are going to construct a data preparation and modeling pipeline, and when we start writing the code, you’ll see and understand the bigger picture of the explanations above.

We will be using scikit-learn, popularly known as sklearn.

We are going to use it to automate a standard ML workflow. You can read more about it in the sckit-learn documentation.

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One major problem to avoid in your work as an ML practitioner is data leakage, and for us to avoid that, we need to properly prepare our data and get comprehensive knowledge about it. Pipelines help us achieve this by making sure that standardization is constrained to each fold of your cross-validation procedure.

We’ll build a simple pipeline that standardizes our data, then create a model that we will evaluate with a leave-one-out cross validation. The next step will be building a pipeline for feature extraction and modeling. 

After we have imported our data, the next step is to load our dataset:     

Next, we have to create our pipeline:

And the last step is to evaluate the pipeline we just created:     

Next, we will build a pipeline for feature extracting and modeling. It is very important to avoid data leakage. Feature extraction can be seen as the act of reducing the number of resources required to describe a voluminous dataset. When carrying out an analysis of complex data, there are 4 steps:

  1. Feature Extraction with Principal Component Analysis
  2. Feature Extraction with Statistical Selection
  3. Feature Union 
  4. Learn a Logistic Regression Model

Now let’s build a pipeline that will extract features and them build a model.

First, we’ll load our data:

Next, we will create a feature union:

Lastly, we have to create and evaluate:


Now that looked very easy, not because pipelines are that easy, but because I chose to do the most beginner-friendly tutorial. This should give you a head start and an idea of pipelines.

Further Reading

Airflow to Orchestrate Machine Learning Algorithms

Building Your First Machine Learning Model Using KNIME

This UrIoTNews article is syndicated fromDzone

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