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What is a Machine Learning Pipeline?

Machine Learning Pipeline

A machine learning pipeline is a series of processes that prepare and analyze data for machine learning model development. It automates the workflow of extracting data from its source, cleaning and preprocessing the data, training and evaluating models, and deploying the models into production[5]. Implementing machine learning pipelines provides many benefits for data science teams and organizations looking to scale their machine learning efforts[10].

What Does a Machine Learning Pipeline Do?

A machine learning pipeline codifies the end-to-end workflow for creating machine learning models. This includes[2][5]:

  1. Extracting raw data from sources like databases, cloud storage, APIs, or web scraping
  2. Preprocessing and cleaning the data by handling missing values, converting data types, normalizing features, etc.
  3. Performing feature engineering to extract and derive meaningful features from the raw data
  4. Splitting the data into training and validation/test sets
  5. Training machine learning models on the data
  6. Evaluating model performance on the validation set
  7. Tuning model hyperparameters to optimize performance
  8. Comparing and selecting the best performing model
  9. Deploying the model to make predictions in production environments
  10. Monitoring the model’s predictions and performance in production
  11. Retraining and updating models as new data becomes available

Automating these tasks through a pipeline removes the need for repetitive manual work for each model developed. It also enforces consistency in how models are developed across an organization[7].

Benefits of Using Machine Learning Pipelines

Implementing machine learning pipelines provides many advantages:

Faster Experimentation and Iteration

With the workflow codified into reusable components, data scientists can quickly experiment with different algorithms and parameters rather than redoing preprocessing and training manually each time. Pipelines allow you to iterate faster.

Consistency and Reliability

Pipelines enforce consistent processes for how data is handled and models are trained. This improves reliability and reduces human error that can occur with ad-hoc workflows.

Collaboration and Modularity

Splitting the ML workflow into modular steps makes it easier for team members with different skillsets to collaborate. Data engineers can focus on data pipelines while model developers work on training and evaluation.

Portability and Reusability

Components of the pipeline can be reused across different projects. New data can be inserted into existing pipelines to quickly train new models.


Pipelines simplify updating workflows over time. If a data source changes format, you only need to update code in one place rather than multiple notebooks.

Version Control and Auditability

Pipeline steps and results can be version controlled and logged, providing transparency into how models are built.


Once pipelines are established, they can be easily scaled up to train and evaluate more models in parallel. Automation enables taking on more machine learning projects.

Monitoring and Observability

Running pipelines on managed platforms allows real-time monitoring of runs. Metrics can be tracked at each step to monitor data and model performance.

Automated Re-Training

Pipelines can be scheduled to trigger new model training runs automatically when new data arrives or model performance declines. This simplifies model maintenance.

Key Components of a Machine Learning Pipeline

While machine learning pipelines can take many forms depending on the specific use case, most pipelines contain a few key components:

Data Extract, Load, and Validate

Raw data needs to be extracted from its sources via methods like database connections or APIs. Data is loaded into the pipeline and basic validation checks are done to ensure the data matches expectations for schema, formats, etc.

Preprocess and Clean

The raw extracted data then goes through a preprocessing and cleaning step. Here the data is prepared for modeling by handling missing values and outliers, normalizing features, converting data types, joining with other datasets, etc.

Feature Engineering

In this step, new features are derived from the raw data to create meaningful inputs for modeling. Domain expertise is applied to extract informative features. Simple examples include calculating time windows or ratios between values.

Train, Evaluate, and Tune Models

This stage trains machine learning models on the prepared data, evaluates them on a holdout set, and tuning hyperparameters to optimize performance. Multiple algorithms like random forest, neural networks, etc. may be tried.

Select and Register Best Model

Based on evaluation metrics, the best performing model is selected and registered. Model registry platforms track metadata like model signatures, metrics, versions, etc.

Deploy and Monitor Models

The registered model is deployed into production applications and prediction APIs. Monitoring tools track the model’s live performance on key metrics to check for data drift or degraded predictions.

Retrain Models

If model performance declines, new training runs are triggered. The pipeline refits models on new data and replaces underperforming models to keep predictions accurate.

Design Principles for Machine Learning Pipelines

Certain design principles should be kept in mind when developing machine learning pipelines:

Tools for Building Machine Learning Pipelines

There are many open source tools and commercial platforms available for building and running machine learning pipelines. Here are some popular options:

General Pipeline Tools:

ML-Focused Pipeline Tools:

Commercial MLOps Platforms:

Implementing Machine Learning Pipelines

The steps to implement ML pipelines will vary based on the tools and infrastructure chosen. However, the general process includes:

  1. Understand the Business Problem – Clearly define the business problem or use case. Determine the desired inputs and outputs of the machine learning system.
  2. Assemble the Data – Identify the data sources and extract the initial raw datasets. If additional data needs to be collected, steps should be added to gather it.
  3. Explore and Clean the Data – Perform exploratory analysis to understand the distributions, correlations, data types and potential issues with the datasets. Clean the data by handling missing values, removing outliers, normalizing columns etc.
  4. Engineer Features – Use domain expertise to derive informative features from the raw data. Create samples for experimenting with feature engineering before implementing changes in the pipeline.
  5. Determine Modeling Techniques – Based on the use case and available data, decide on what types of models to train – classification, regression, clustering, etc. Choose 1-2 promising modeling techniques to begin with.
  6. Build the Pipeline – Construct the pipeline by breaking down the workflow into logical, reusable steps based on the design principles. Use a pipeline framework and modular code.
  7. Test and Validate – Run test data through the full pipeline and validate that the preprocessed data and model outputs match expectations. Fix any issues before training on all data.
  8. Integrate with Production – Once the pipeline operates end-to-end, integrate it with production data sources and applications to deploy trained models. Monitor its operation.
  9. Retrain and Improve – Once in production, watch for degraded model performance and trigger retraining when needed. Iterate on features and modeling techniques to improve it over time.

Real World Machine Learning Pipelines

Machine learning pipelines power many common applications today. Here are some examples:

The Importance of Machine Learning Pipelines

The workflow automation and efficiency gains provided by machine learning pipelines are critical for successfully applying machine learning in many business contexts. As organizations look to scale their ML and AI initiatives across the enterprise, pipelines remove friction in development, increase model velocity, enforce best practices, and improve outcomes. Establishing pipelines lays the foundation for mature MLops processes. Rather than approaching ML as one-off projects, pipelines enable reliable, efficient, and controlled continuous integration of ML models for maximum business impact.

Key Takeaways



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