What Is a Machine Learning Pipeline?

Dec 15, 2023 ·

A machine learning (ML) pipeline is a set of processes used to train, deploy, and manage ML models and other data-driven applications. The goal of an ML pipeline is to enable organizations to quickly and efficiently develop predictive models for their business or research needs.

An ML pipeline typically starts with data ingestion from multiple sources, and then moves through a series of steps such as data cleaning, feature engineering, model training, and model testing. In most cases, the end product of an ML pipeline will be a model that can be deployed in a production environment.

Data ingestion is the first step of any ML pipeline. It involves collecting data from various sources and formatting it into a usable format. Commonly used formats for data ingestion are CSV (Comma Separated Values), JSON (JavaScript Object Notation), XML (Extensible Markup Language), and RDF (Resource Description Framework).

Once data has been ingested, data cleaning is usually performed. This step involves removing irrelevant or incomplete data, handling missing values, and identifying outliers. Data cleaning helps improve the performance of the ML models and reduce the risk of incorrect predictions.

The next step in the ML pipeline is feature engineering. This process involves selecting the most relevant features for the target prediction and transforming the initial data into something more suitable to machine learning algorithms. Feature engineering can help improve prediction accuracy and make ML models more interpretable.

After feature engineering, model training is the next step. This process usually involves building a model architecture, choosing a suitable optimizer, defining loss functions, and training the model on training data. During this step, different hyperparameters such as learning rate, number of hidden layers, and regularization strength are also adjusted.

Finally, model testing and evaluation is performed. This is done by evaluating the trained model on a set of test data and comparing the results with the baseline model. Model testing helps identify any issues with the model and determine if there is a need to improve or refine the model before deployment.

Overall, an ML pipeline is an automated process used to build predictive models. By using a combination of data ingestion, data cleaning, feature engineering, model training, and model testing, organizations can quickly develop ML models that are ready for deployment.