Machine Learning Pipelines: What They Are, Importance, Examples, and Uses Cases

What is a machine learning pipeline?

A machine learning (ML) data pipeline is an end-to-end process that automates the building, training, deploying, and maintaining of ML models. It connects steps like data processing, feature engineering, model training, and prediction outputs in a seamless workflow, where each step’s output becomes the input for the next. This streamlines complex processes, enabling scalability, consistency, and improved model accuracy for data scientists and engineers.

Why are machine learning pipelines important?

Data pipelines for machine learning are important for managing complexity. Pipelines typically have multiple steps, each with unique requirements, such as different libraries and runtimes. They may also need to execute on specialized hardware profiles. ML pipelines allow you to factor these considerations and requirements into development and maintenance.

Benefits of machine learning pipelines

Machine learning (ML) pipelines offer transformative benefits that help data scientists, engineers, and organizations by streamlining and optimizing every stage of the ML workflow.

• Boosted efficiency and productivity: Automating tasks like data preprocessing, feature engineering, and model training reduces manual effort, saving time and resources while minimizing human error.
• Enhanced reproducibility: Standardized workflows and experiment tracking ensure consistent results and simplify replicating processes.
• Improved collaboration: A structured pipeline fosters better teamwork, enabling all members to work with the same up-to-date data and processes.
• Modular and scalable design: Pipelines allow teams to isolate and optimize individual steps, making it easier to adjust workflows for large datasets or complex models without rebuilding from scratch.
• Support for experimentation: Teams can experiment freely by tweaking pipeline components, such as preprocessing techniques or model architectures, to refine results.
• Faster, more reliable predictions: Automation accelerates predictions, enabling quicker, data-driven decision-making in real-world applications.

ML pipelines empower organizations to handle complexity, enhance scalability, and free up valuable resources for innovation, driving impactful machine learning solutions at scale.

Steps to building a machine learning pipeline

If you’re interested in building an ML pipeline to improve consistency, reduce repetitive tasks, and more, here are the key steps at a high level.

1. Data collection

ML relies on data, so the first step is to collect it from all relevant sources, such as databases, APIs, and files. It’s crucial to make sure that the data is high-quality and does not have missing values, duplicate information, or other errors.

2. Data preprocessing

If you’re working with raw data, you may need to preprocess the data. This step converts the raw data into a clean, structured format so it can be used for analysis and model training.

3. Feature extraction and engineering

In this third step, you convert the raw data into useful features to drive the ML model’s predictive capabilities (i.e., feature extraction).

4. Model selection

Model selection refers to the process of evaluating, comparing, and choosing the most ideal model to meet data and problem requirements.

5. Model training and evaluation

Next up is model training and evaluation. In the model training stage, you will train the ML model to make predictions based on the data you’ve prepared.

6. Model deployment

Once the ML model has been evaluated and found to perform satisfactorily, it can be deployed in a production environment.

7. Monitoring and maintenance

Finally, the ML model will need to be monitored continuously and maintained over time.

Use cases and examples of machine learning pipelines

As machine learning expands into multiple domains and applications, there are a growing number of relevant use cases.

Data Collection

An example of data collection is gathering data from all relevant sources.

Data preprocessing

Once you’ve collected your customer churn data, you may find that it is not all suitable for your machine learning data pipeline.

Feature extraction and engineering

In the use case of customer churn prediction, you would select or engineer features relevant to your goal.

History and evolution of machine learning pipelines

Throughout history, as machine learning and data science have advanced, so has the evolution of machine learning pipelines. Data processing workflows pre-date the 2000s and were primarily used for data cleaning, transformation, and analysis. Unlike today’s workflows, they were largely manual or reliant on spreadsheets.

Machine learning pipelines came to be right around the 2000s. Prior to automated workflows, data scientists and researchers used manual processes to manage machine learning tasks. In 1996, the Cross-industry standard process for data mining (CRISP-DM) was defined as a standard process for data mining. It breaks down data mining into six phases and largely governed the management of ML workflows:

1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Evaluation
6. Deployment

Workflows continued to be made more systematic and automated as machine learning advanced as a field. Data science entered as a concept across disciplines in the late 2000s. As such, data scientists formalized related workflows and introduced preprocessing, model selection, and evaluation to pipelines. In the 2010s, machine learning libraries and tools emerged. This allowed data scientists and other practitioners to more easily create and evaluate machine learning data pipelines. During this time, there was a greater emphasis on scalable pipelines due to more big data technologies.

In the 2010s, the concept of automated machine learning (AutoML) came to the forefront. Practitioners now had more tools and platforms available to automate the building, deployment, and management of machine learning pipelines and related tasks. During this time, machine learning pipelines were also integrated with DevOps practices. This integration allowed for continuous integration and deployment (CI/CD) models, known as machine learning operations (MLOps).

The concepts of containerization and microservices became more popular during this time period as well. Docker was released in 2013 and is one of the top platforms for containerization due its facilitation of packaging and deploying software apps. Kubernetes emerged in 2014 and automates tasks associated with containerized apps, including machine learning workloads.