Machine Learning Pipelines helps in automating the process of the lifecycle of a machine learning model. It automates the lifecycle of data validation, preprocessing, training and deployment on a new dataset. In this article, I will take you through Machine Learning Pipelines and its implementation using Python.
What are Machine Learning Pipelines?
A machine learning pipeline is a simple way to keep the entire process of training a machine learning model in a very organized way. Think of a machine learning pipeline as a collection of all the steps you use to train a machine learning model, and a pipeline can be used in a single step on a new set of data while working on the same kind of problem.
Also, Read – 200+ Machine Learning Projects Solved and Explained.
Besides automating the process of training a model on a news dataset, machine learning pipelines provides more advantages such as:
- It provides the opportunity to focus on training new machine learning models for more problems and not stick to the same type of problem.
- It helps in prevention of bugs.
- It helps in spending more time working on new problems.
- It helps in updating existing models very easily.
Machine Learning Pipelines using Python
Machine learning pipelines include all the steps that we need to use in general when training a machine learning model, such as:
- Data Collection
- Data Cleaning
- Feature Extraction
- Model Validation
Now let’s see how to implement a machine learning pipeline using Python. The Scikit-Learn library in Python provides “sklearn.pipeline” which we can use to implement a machine learning pipeline using Python. Now let’s import the dataset and start with the task of implementing machine learning pipeline using Python:
[1663200. 1921000. 2922000. … 1813470. 373000. 1144000.]
As you can see, I started this task by splitting the data into training and test sets. In a realtime machine learning task, we do this step after completing data cleaning, feature engineering and validation. So in a machine learning task, you have to implement a machine learning pipeline while training a model.
So this is how you can implement a machine learning pipeline using the Python programming language. I hope you liked this article on what is a machine learning pipeline and its implementation using Python. Feel free to ask your valuable questions in the comments section below.
What is Feature extraction and model validation?
The process of choosing the best features for the performance of Machine Learning model.