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

What is a machine learning pipeline?

A data pipeline for machine learning combines data processing and modeling steps to automate the building, training, deploying, evaluation, and maintenance of machine learning models. This end-to-end construct is designed to streamline complex machine learning processes for greater efficiency. Machine learning pipelines include the raw data input, features, related machine learning model and model parameters, and prediction outputs. Data scientists, engineers, and analysts use these pipelines to standardize the workflow for consistency, facilitate scalability, and improve the accuracy and reliability of these models. 

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

There are several benefits to data pipelines for machine learning, including:

• Improved efficiency and productivity in the development and deployment of machine learning models.
• Enhanced reproducibility of machine learning workflows and experiments, allowing for more consistent results.
• Consistency for all team members to work with the same, most up-to-date data and processes.
• Greater room for experimentation by modifying steps in the pipeline to try different data preprocessing techniques, models, etc. 
• Automation of routine tasks, such as data preprocessing, feature engineering, and model training processes, saving time and resources while helping reduce human error.
• The modularization of steps within the ML pipeline allows you to isolate each step to develop, test, and optimize individually.
• Greater scalability for large datasets and complex workflows; adjust pipelines as needed to suit data and complexity without having to build anew each time.
• Enhanced collaboration thanks to a structured, organized workflow, which allows for mutual understanding.
• Easier integration of pipelines into applications and systems.
• Reduced burden on resources with more time to work on value-added tasks instead of manual, repetitive ones.
• Increased speed of predictions through automation, allowing for more improved data-driven decision-making.

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. 

• 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. 

• 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. You’ll need to address missing values, remove duplicates, correct inconsistencies, normalize and encode features, and merge datasets from multiple sources. You also need to split the data into training and testing sets at this stage. As it sounds, the training set is used to train the ML model. The testing set evaluates the performance of the ML model. 

• 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). You may also select existing features from the data (i.e., feature engineering). There are multiple techniques that can be used for feature extraction, such as Principal Component (PCA), text feature extraction, image feature extraction, wavelet transform, and Fourier transform. The technique you select will depend on the type of data and your goal. 

As far as feature engineering is concerned, this step helps the ML model learn data patterns and improve performance. Some popular techniques for extracting features include polynomial features, binning, log transformations, data and time features, and aggregations. 

For both feature extraction and engineering, it is important to evaluate the features with the goal of understanding their impact on model performance. 

• Model selection

Model selection refers to the process of evaluating, comparing, and choosing the most ideal model to meet data and problem requirements. You will select the machine learning model from multiple candidate models to suit specific predictive tasks. In other words, you want to ensure the ML algorithms suit the problem type, data, and performance requirements. It’s helpful to consider the problem type first, such as classification, regression, clustering, etc. You will also want to identify metrics to evaluate the model performance. 

• 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. At this point, you should have already selected your machine learning algorithm (e.g., linear regression, neural network). The model will learn patterns and relationships within the data. Post-training, you will evaluate the model using a different testing dataset or cross-validation. The goal of this step is to ensure that the model can perform well with new data (instead of just your training dataset). 

• Model deployment

Once the ML model has been evaluated and found to perform satisfactorily, it can be deployed in a production environment. This multi-step process includes serving the model, integrating it with production systems, and evaluating its performance in the production environment. This stage is critical to ensuring the machine learning data pipeline can function in a real-world environment. 

• Monitoring and maintenance

Finally, the ML model will need to be monitored continuously and maintained over time. Retraining may be necessary to maintain its accuracy as data patterns change. 

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. For the purpose of this article, we’ll break down examples of machine learning pipelines as they relate to the first three steps of the building process detailed above. 

Data Collection

An example of data collection is gathering data from all relevant sources. For example, if you want to predict how many customers will churn from a business, you would gather related data from your web analytics, CRM, and transactional processes. 

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. You would need to clean the data by removing duplicates, addressing errors, and structuring the formatting so it can be processed. 

Feature extraction and engineering

In the use case of customer churn prediction, you would select or engineer features relevant to your goal, such as customer tenure, payment history, and customer support logs. 

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.