Machine learning pipeline: What it is, Why it matters, and Guide to Building it?

10 min readMay 23, 2023

As many organization knows, training and testing models in any real-life data are not only the solution for them. Making these trained model work in real-life conditions is something the exact solution. To make such ML models work in real-life use cases, we are required to codify various components together in such a way that they can automate the workflow to reach the desired outcome. Here the concept of machine learning pipelines comes into the picture, using which organizations can not only take out desired outcomes but also keep their system healthy and flawless by monitoring production. In this article, we will delve into the space of machine learning pipelines using the following important points:

What is a machine learning pipeline?
What are the Components of a Machine Learning Pipeline?
Why machine learning pipelines matter?
Practices to Follow When Building a Machine Learning Pipeline

What is a Machine Learning Pipeline?

We can think of machine learning pipelines as a sequence of interconnected steps or processes involved in developing and deploying a machine learning model. When we dig down into an ML pipeline, we find it encompasses the entire workflow, from data preparation and preprocessing to model training, evaluation, and deployment. The steps and processes under such a pipeline contribute to the overall development and optimization of the machine learning model.

For most of the data science team, the ML pipelines need to be the central product as it can encapsulate all the best practices for building machine learning models to take ML models to production while ensuring the highest quality and scalability. One noticeable thing here is that by using a single machine-learning pipeline, teams can productize and monitor multiple models even when the models need to be updated frequently. So to successfully run ML applications, an end-to-end machine learning pipeline is a necessity.

What are the Components of a Machine Learning Pipeline?

As discussed above, there are steps or processes involved in building an end-to-end machine-learning pipeline. We can consider these processes and step as the components of a machine learning pipeline. A typical machine-learning pipeline consists of the following components:

Data Preparation

Data collection: this component of the ML pipeline ensures that the data we are going to use for training models are stored in a place, or it can also be the streaming data. In the case of non-streaming data, we store the data in places like data warehouses and data lakes. But when streaming data is applied to a machine learning system, we use various techniques such as a data ingestion system, event-driven architecture, APIs and webhooks to collect the data so that data can be fed into the subsequent stages of the ML pipeline. More on Machine learning pipelines with streaming data will be discussed in the next topics. Here we will make the concept of the machine learning pipeline stronger. So let’s take a look at the next components.
Data Processing: we use this component to make data clean and transformed suitable for model usage, such as training testing and validating. This component may include processes like handling missing values, outlier detection, data normalization, and feature scaling.
Feature Engineering: This component directly impacts the performance of the model as it is the last component of the data preparation part which includes Creating new features or selecting relevant features from the available data. Here different techniques, such as one-hot encoding, feature scaling, dimensionality reduction, and creating interaction terms, can be involved.

Model Building

Model Selection: This component includes the process of choosing an appropriate machine learning algorithm or model based on the problem requirements and characteristics of the data. Here teams take time to iterate with multiple models and respective parameters.
Model Training: here, prepared data and selected machine learning models get combined and train the model by optimizing its parameters or weights to minimize a specific loss function.
Model Evaluation: this component of the pipeline help assess the performance of the trained model on unseen data using appropriate evaluation metrics such as accuracy, precision, recall, F1-score, or others depending on the problem type (classification, regression, etc.).
Hyperparameter Tuning: this component is responsible for fine-tuning the hyperparameters of the selected model to optimize its performance. This can be done through techniques like grid search, random search, or Bayesian optimization.

Model Deployment

Model deployment: this component is responsible for deploying the model into the production environment. Generally, data scientists prefer to create APIs and web services of models to deploy them, or they also deploy the models in an application or server.
Monitoring and Maintenance: this is the last component of any ML pipeline, which involves Continuously monitoring the deployed model’s performance, retraining the model periodically with new data, and making necessary updates or improvements based on feedback and changing requirements. However, we don’t consider it as the last step to take because this takes regular treatments.

After knowing the key components of machine learning pipelines, let’s take a look at why ML pipeline matters.

Why does the machine learning pipeline matter?

In the above, we got to know what exactly a machine learning pipeline is and what are the components that it involves in it. By just going through this, we can have a glimpse of its importance. Still, there are some points which we need to clarify here. So let’s take a look at the reasons which show the essentialness of the machine learning pipeline:

Efficiency and Productivity:

Streamlined Development: Machine learning pipelines provide a structured and organized approach to model development, allowing data teams to work more efficiently.
Automated Processes: Pipelines enable the automation of repetitive tasks such as data preprocessing, feature engineering, and model evaluation, which directly impact time and effort reduction.
Rapid Iteration: Pipelines enable quick experimentation by easily swapping components, testing different models or hyperparameters, and iterating on the pipeline design.

Reproducibility and Consistency:

Reusable Components: Pipelines promote the reuse of data preprocessing, feature engineering, and model training code, ensuring consistent results across different iterations or team members.
Version Control: Pipeline components can be tracked and managed using version control systems, allowing for reproducibility and easy collaboration.

Scalability and Performance:

Scalable Processing: Pipelines handle large datasets by distributing processing across multiple machines, enabling efficient scaling for training and inference.
Parallel Execution: Pipelines can execute multiple stages or components in parallel, reducing overall processing time and improving performance.
Resource Optimization: Pipelines manage resources efficiently by optimizing memory usage, minimizing computational redundancies, and leveraging distributed computing frameworks.

Deployment and Productionization:

Seamless Deployment: Pipelines facilitate the integration of trained models into production systems, enabling easy deployment as APIs, web services, or real-time applications.
Model Versioning: Pipelines support model versioning, allowing for easy tracking and managing deployed models, making updates and rollbacks straightforward.
Monitoring and Maintenance: Pipelines can include monitoring components to track model performance, detect anomalies, and trigger retraining or updates as needed.

Collaboration and Governance:

Team Collaboration: Pipelines foster collaboration by providing a common framework and structure for data scientists, engineers, and domain experts to work together.
Governance and Compliance: Pipelines can incorporate checks and validations to ensure compliance with regulations, data privacy, and ethical considerations.

Experimentation and Model Selection:

Iterative Development: Pipelines enable rapid iteration and experimentation by facilitating easy testing of different models, hyperparameters, and feature engineering techniques.
Performance Evaluation: Pipelines provide mechanisms for evaluating and comparing models based on predefined metrics, aiding in informed decision-making.

Now that we know the benefits of applying machine learning pipelines let’s learn about the practices which we should follow when seeking to apply machine learning pipelines for ML workflows.

Practices to Follow When Building a Machine Learning Pipeline

Machine learning pipelines increase the iteration cycle and give confidence to data teams; however, when we talk about building a machine learning pipeline, the starting point may vary for different teams, but it is important to follow certain practices to ensure efficient development, reproducibility, scalability, and maintainability. Here are some best practices to consider:

Define Clear Objective: when building machine learning projects, it is necessary to define the problem statement, goals, and success criteria of the whole workflow. Understanding the business need and expectations may guide us to a better development of ML pipelines.
Data Preparation: however, many teams do not consider this step as part of the ML pipeline, but before taking any data from the ML pipeline, it is necessary to Perform thorough data exploration and preprocessing. Handle missing values, outliers, and inconsistencies. Normalize, scale, or transform features as required. Split data into training, validation, and test sets for model evaluation comes in between the pipeline, and we make codes available for this in-between.
Modular Pipeline Design: we can break the pipeline into modular components such as data preprocessing, feature engineering, model training, and evaluation. While doing this, we should make these components well-defined, encapsulated, and reusable. There are frameworks and libraries, such as sci-kit-learn, TensorFlow Extended (TFX), or Apache Airflow, which can help us with modular pipeline design.
Version Control and Documentation: In the machine learning pipeline, version control tools such as helps Git to track changes, configuration files, and metadata enables reproducibility, collaboration, and easy rollback to previous versions if needed. Here documentation of pipeline components, dependencies and configuration settings explains the purpose, inputs, outputs, and usage of each component. As well as it helps in understanding and maintaining the pipeline.
Hyperparameter Tunning and Experiment Tracking: automating the process of hyperparameter tunning using techniques such as grid search, random search, or Bayesian optimization not only helps to explore different hyperparameter combinations but also saves time and effort. After that, enabling experiment tracking makes it easy to record and compare different model configurations, hyperparameters, and evaluation metrics. Tools like MLflow can help track experiments and visualize results.
Model Evaluation and Validation: The use of appropriate evaluation metrics and validation techniques helps in assessing model performance. Techniques such as Cross-validation, stratified sampling, or time-based splitting can be used depending on the data characteristics.
Performance Monitoring and Maintenance: Continuously monitoring the performance of deployed models and data ensures less chance of failures in the pipeline. So setting up a system to detect anomalies, concept drift, or degradation in model performance becomes necessary.
Security and Privacy: Ensuring data security and privacy throughout the pipeline is a compulsion. Implement measures to handle sensitive data, anonymize or encrypt data where required, and adhere to privacy regulations such as GDPR or HIPAA.
Continuous Integration and Deployment: here, we also need to implement continuous integration and deployment (CI/CD) practices to automate testing, building, and deploying the pipeline. There are tools like Jenkins, GitLab CI/CD, or Azure DevOps which can help you enable CI/CD in ML pipelines.

Here are the key practices following which we can build and deploy an efficient ML pipeline. Now let’s take a look at why we are discussing this topic.

Why are we on this topic?

We at DSW | Data Science Wizards understand that building end-to-end machine-learning pipelines is a challenging task to perform. There are various factors which we need to cater to during this development; addressing these challenges requires a combination of technical expertise, domain knowledge, collaboration, and iterative development processes. It is important to anticipate and proactively tackle these challenges throughout the pipeline development lifecycle to build robust, scalable, and efficient machine learning solutions.

To pass over such hurdles, we have built a solution platform UnifyAI, which is an advanced platform designed to address the challenges organizations face when transitioning their AI use cases from experimentation to production. Built with a strong focus on the above best practices for building efficient machine learning pipelines, UnifyAI offers a comprehensive solution to streamline and accelerate the deployment of AI models.

With UnifyAI, organizations can not only overcome the challenges associated with building ML pipelines but also experience various benefits such as End-to-End Pipeline Management, modular and flexible architecture, built-in best practices, better collaboration and governance and many more. Some key benefits of UnifyAI are as follows:

It provides all the necessary components to transform and evolve data science and AI operations from experimentation to scalable execution.
Using UnifyAI organization can eliminate repetitive data pipeline tasks and saves over 40% of your time creating and deploying new AI-enabled use cases, allowing you to focus on driving business growth.
Its unified data and model pipeline reduces overall TCO by up to 30% as organizations consider scaling their AI and ML operations.
Its well-designed monitoring system provides greater control over your data flow, models, and system performance.

In short, UnifyAI empowers organizations to unlock the full potential of their AI initiatives, enabling them to make informed decisions, drive innovation, and deliver impactful results across various industries and domains. To discover more about UnifyAI, Connect with our team. The details about us are given below.

About DSW

DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.

Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.

Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.