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...