Pipeline in Sklearn

“It’s crucial for application development to create Machine Learning (ML) algorithms quickly and effectively. Before prediction, data goes through a number of data processing processes. We require a method to quickly process our data by combining several processes into a single series. The ML pipeline comes here in practice. Using this technique, we can easily incorporate our algorithms and data processing stages into a single series. We’ll talk about the ML pipeline, its requirements, and its implementation with sklearn.”

What is the Machine Learning Pipeline?

A pipeline is a collection of algorithms chained, concatenated, and scrambled in some way to handle a stream of data; it contains inputs and outputs. It may or may not contain a state as well. A machine learning algorithm takes clean data and learns a pattern to predict fresh data. As a result, you’ll need to preprocess that data to provide input data for the machine learning algorithm. Similarly, the ML algorithm’s output is only a number in the software that must be analyzed to do some action in the real world. You will have to do the same thing again and again without a pipeline. This is where the pipeline comes in; you can combine all of these actions into a single container in a step-by-step fashion so that once the data is imputed to the pipe, the operations are carried out sequentially until the correct data format is selected.

Why Machine Learning Pipelines?

Organizations can use machine learning models to discover opportunities and hazards, improve their company strategy, and provide better customer service. However, it is time-consuming to acquire and process data for machine learning models, utilize it to train and test them, and finally operationalize.

Companies want their data science teams to produce relevant business predictions sooner by speeding up the process. Machine learning pipeline monitoring allows you to operationalize machine learning models faster by automating procedures. Machine learning pipeline orchestration reduces the time it takes to create a new machine learning model and also helps increase the quality of your models. Although we refer to it as a pipeline, genuine pipelines are one-way and one-time only, which is not the case with machine learning pipelines. ML pipelines are iterative cycles in which each step is repeated several times. ML pipelines use CI/CD techniques to improve the accuracy of ML models and the quality of your algorithms. Data scientists from all industries employ automated machine learning processes to improve their models and accelerate development and deployment. Companies of all sizes see the advantages that machine learning models may provide in every department. Marketing, sales, product, and customer care departments are among the departments that want to use machine learning to analyze their data. Still, only major businesses can afford to staff a data science team large enough to handle all requests. A machine learning CI/CD pipeline can help a tiny data science team punch above its weight. Pipelines democratize access to machine learning models, allowing even small businesses to use machine learning to improve data-driven business choices.

Advantages of Machine Learning Pipeline

Improve the Customer Experience

You can develop machine learning models faster and apply them to more use cases with machine learning orchestration, allowing you to predict rather than react to consumer trends and understand customer preferences at a granular level, providing a better customer experience and increasing your bottom line.

Improve Data-Driven Decision-Making

Machine learning predictions enhance decision-making and add value to every part of your organization. However, building a model for each request can be time-consuming for the data science team. ML pipelines allow teams to break down silos and use AI predictions for better data-driven decision-making.

Allow Time for Your Data Science Team to Work

It’s uncommon to come across a company with a large data science staff to reply to everyone’s request for machine learning predictions for their use cases. Machine learning pipelines take care of many time-consuming duties that can be automated, thus, allowing them to focus on work that cannot be automated.

Improve Your Company Strategy

Machine learning in the CI/CD pipeline aids in developing more accurate machine learning models for your business management team to utilize in identifying opportunities, mitigating risks, and tracking demand, ensuring that your strategy keeps you ahead of the competition.

Implementing Pipeline in Sklearn

Importing required classes and methods

from sklearn.linear_model import LogisticRegression

from sklearn.preprocessing import StandardScaler

from sklearn.datasets import make_classification

from sklearn.model_selection import train_test_split

from sklearn.pipeline import Pipeline

Creating a sample dataset

X, y = make_classification(random_state=42)

print('Features are', X)

print('Labels are', y)

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)


Features are

[[-2.02514259 0.0291022 -0.47494531 ... -0.33450124 0.86575519


[ 1.61371127 0.65992405 -0.15005559 ... 1.37570681 0.70117274

-0.2975635 ]

[ 0.16645221 0.95057302 1.42050425 ... 1.18901653 -0.55547712



[-0.03955515 -1.60499282 0.22213377 ... -0.30917212 -0.46227529


[ 1.08589557 1.2031659 -0.6095122 ... -0.3052247 -1.31183623


[-0.00607091 1.30857636 -0.17495976 ... 0.99204235 0.32169781

Labels are [0 0 1 1 0 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0

0 1 1 1 0 1 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 0 0 1 0 1 0 1 0

1 1 1 0 0 0 1 0 1 0 1 1 1 1 1 0 0 1 0 1 1 0 1 1 0 0]

Creating a series of algorithms using the pipeline and fitting the training data on the pipeline

pipe = Pipeline([('scaler', StandardScaler()), ('lr', LogisticRegression())]), y_train)

Pipeline(steps=[('scaler', StandardScaler()), ('lr', LogisticRegression())])

pipe.score(X_test, y_test)




We discussed the ML pipeline description, its uses, advantages, and implementation in sklearn. ML pipeline incorporates multiple algorithms into a single series, allowing us to write our code in a more quick and efficient manner. It can also embed data preprocessing and model-building steps into a single series.

About the author

Simran Kaur

Simran works as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Travelling, sketching, and gardening are the hobbies that interest her.