Building Machine Learning Pipelines

With the rapidly growing demand for machine learning models, there is an increasing need for a more efficient and streamlined approach to developing, deploying, and maintaining these models.
Pavel Klushin
Pavel Klushin
Head of Solution Architecture at Qwak
February 14, 2023
Table of contents
Building Machine Learning Pipelines

With the rapidly growing demand for machine learning models, there is an increasing need for a more efficient and streamlined approach to developing, deploying, and maintaining these models. This is where MLOps (ML Operations) comes into play. MLOps is a set of practices that unify the collaboration between data scientists and operations teams, allowing for the seamless integration of ML models into production systems. 

Machine learning pipelines are crucial in the MLOps process, as they automate the end-to-end process of developing, testing, and deploying ML models. A well-designed ML pipeline streamlines model production and ensures its quality, consistency, and scalability.

This article will delve into the various stages involved in building an ML pipeline and its importance, starting from data ingestion to model deployment and testing. We will also discuss several best practices for you to ensure the peak performance of your ML pipeline. By the end of this article, you will have a comprehensive understanding of the process of building a robust ML pipeline that delivers high-quality, scalable models.

What is a machine learning pipeline? 

Machine learning (ML) pipelines are automated workflows that streamline the process of training and deploying ML models. These workflows enable data to be efficiently transformed and correlated into a model that can be analyzed for optimized outcomes.

A typical ML pipeline consists of multiple sequential stages that take care of everything it takes to build an ML model, from data extraction to model deployment and monitoring. Each stage represents an ML process designed as a stand-alone module, where all the modules come together to get the finished product. Some of the key elements of an ML pipeline include:

Raw input data

  • Model parameters
  • Training outputs
  • Features
  • Models
  • Predictions & Outcomes

The goal of an ML pipeline is to standardize and automate ML implementation, reducing the time and effort required to develop and deploy ML models. This standardization ensures that all models of an organization follow the same process, reducing the likelihood of human error and promoting consistency. It aims to help organizations achieve faster and more accurate model training, improved collaboration, and increased efficiency in their ML processes. 

Approaches to building ML pipelines

Based on the different approaches to data processing and model training, the common methods for building ML pipelines include the following:

  • Batch ML pipeline: In this approach, data is processed in large chunks, typically at regular, pre-defined intervals. Batch pipelines generally are simpler to implement and more resource-friendly. They are suitable for projects where data is available in bulk and response time is not critical.
  • Real-time ML pipeline: In this ML pipeline, data is processed to create features and models in near-real-time. Real-time pipelines offer faster and more accurate predictions. They are suitable for projects requiring immediate predictions, such as high-speed data streaming or online applications.

What are the key processes in building a machine learning pipeline?

Building a machine learning (ML) pipeline is a multi-step procedure that involves several key processes, including:

Data Ingestion 

The first step in building an ML pipeline is to ingest the data. Data ingestion involves acquiring data from various sources, such as databases, APIs, or file systems, and storing it in a centralized location. 

Data Preprocessing 

After the data has been ingested, it needs to be preprocessed. This step involves data cleaning, finding and managing missing data, and transforming data into an ML model-suitable format. This step ensures data usability in the next steps of the pipeline.

Model Selection 

The next step is to select the appropriate ML model for the task. It involves evaluating different ML algorithms and selecting the best fit for the data and the task requirements. 

Feature Engineering 

This stage involves creating new features from existing data. It involves aggregating existing data, transforming existing data, or creating new data. Most feature stores offer complete data transformation with built-in feature engineering capabilities.

Model Training 

After the features have been created, the ML models are trained using the preprocessed data, and their parameters are fine-tuned. 

Model Deployment & Validation 

Once the models have been trained, they can be deployed in a production environment, e.g., on a server or in the cloud, with an infrastructure to support the deployment. Validation is essential at this stage for testing the models in a production environment to ensure they are working as expected.

Model Monitoring 

The final step is to monitor the models by tracking their performance and detecting anomalies. This step ensures that the models continue to perform as expected and any issues that arise are addressed promptly.

Why are pipelines important for machine learning? 

Traditional machine learning methods are built upon a monolithic architecture where all ML processes are run together using the same script for data extraction, cleaning, preparation, modeling, and model deployment. The problems with this approach arise when trying to scale this architecture., leading to high workload volume, inefficient use of resources to expand the model portfolio, and manually having to script updates to change workflow configurations. 

On the other hand, an ML pipeline is based on a modular architecture that abstracts away every stage of your ML workflow into independent components. This approach promotes reusability when designing new workflows. So, instead of repeating all ML processes, you can individually call parts of the workflow you need and use them where required. The ML pipeline centrally manages and tracks all its components to handle updates made by different models.

Benefits of ML pipelines

Machine learning pipelines offer many benefits to organizations looking to streamline their ML workflows and improve the quality of their models. Here are some of the many benefits of an ML pipeline:

  • Increased scalability: ML pipelines allow you to break down complex workflows into smaller, more manageable components, making it easier to scale up your ML efforts as your business grows and add new models.
  • Task automation: Automating the various stages of the ML pipeline reduces the manual effort involved, leading to more consistent and efficient workflows and allowing you to focus on higher-level tasks that require human intervention.¬†
  • Near real-time predictions: ML pipelines are designed for quick and seamless integration of new data, processing it in real-time, and feeding it into the models for prediction. This results in near real-time predictions and improved responsiveness to dynamic changes in the data.¬†
  • Efficient utilization of computational resources: ML pipelines allow for efficient use of your organization's computational resources by enabling the reuse of components and sharing intermediate results.
  • High-quality, consistent workflows: ML pipelines work with a standardized data layer that offers a consistent approach to building and deploying models, leading to higher-quality outcomes.
  • Easier to monitor model performance: ML pipelines facilitate monitoring your machine learning workflows to assess your model performance, offering you a better understanding of model behavior to identify opportunities for improvement.

What are the pitfalls to avoid when building machine learning pipelines? 

To leverage the full potential of ML pipelines for your MLOps requirements, you must pay close attention to its design and implementation to avoid structuring an unreliable and insecure pipeline. Here are some common pitfalls to avoid when building machine learning pipelines:

  • Data leakages: Leakages occur when you use information from the test set in the training phase, leading to overly optimistic performance estimates. Use proper data partitioning and ensure that data is pre-processed consistently to avoid data leakages.
  • Data overfitting: Overfitting occurs when you train your model too closely to the training data, causing it to perform poorly on new, unseen data. Techniques such as cross-validation and regularization can help you avoid overfitting.
  • Lack of model versioning: Failing to track the versions of your pipeline and its components can lead to inconsistencies and make it challenging to reproduce consistent results for multiple models.
  • Complex pipeline structuring: Avoid building an overly complicated pipeline with interdependent components. A poorly designed ML pipeline increases maintenance costs, reduces performance, is incapable of scaling to your needs, and is difficult to debug and test.
  • Neglecting continuous monitoring: Without ongoing pipeline monitoring, you lack visibility into potential issues that may arise. Moreover, you will fail to maintain your pipeline performance and keep up with data changes, causing your model performance to degrade over time.

Best practices for implementing robust machine learning pipelines

Here are some key best practices you can follow to build robust ML pipelines that enable efficient and accurate model production:

Standardized data layer for pipeline consistency

Ensuring consistency and reducing the risk of errors requires establishing a standardized data layer for processing and transforming data in the ML pipeline. You can achieve this consistency by:

  • Defining and documenting data preparation and transformation steps.
  • Using data quality checks and standardization tools.
  • Establishing and enforcing consistent column names, data types, and format.¬†

Eliminating inconsistencies and ensuring high data quality can help you produce reliable ML pipelines.

Continuously Monitoring Real-World Data for Drift 

Regularly monitor real-world data for changes in distribution or quality by:

  • Implementing data drift detection algorithms.
  • Setting up automated monitoring systems to detect data changes.¬†
  • Conducting regular data quality and distribution assessments and taking action to address any issues found.

Frequent data monitoring is essential for ensuring the accuracy and reliability of your ML pipeline. It helps you identify and resolve any issues in the pipeline before they negatively impact your model accuracy. You can ensure your ML pipelines remain effective and accurate over time by continuously monitoring real-world data for drift.

Automating Pipeline Workflows

Automate as many steps in the ML pipeline as possible by:

  • Using workflow automation and management tools and processes for data preparation, feature extraction, model training and deployment, and other tasks that can be executed without human intervention.
  • Setting up automated triggers to execute tasks triggered by specific events, creating an automated workflow, for example, triggering model retraining whenever new data enters the pipeline.

Automation is key in building a robust ML pipeline that can help you save time and increase business efficiency by freeing up organizational resources for more critical tasks.

Model Versioning 

Implement model versioning in your ML pipeline to track changes in your models by:

  • Using Git or other version control systems.
  • Maintaining a comprehensively documented record of the model versions.

Model versioning makes it easier to identify problem areas and resolve them. Additionally, you can ensure the availability and use of the latest model version, reducing the risk of errors and ensuring that the pipeline remains up-to-date.

Ensure reproducibility

Make your ML pipeline reproducible by:

  • Clearly documenting pipeline stages with organized code, data, and configuration information.
  • Maintaining version control and change tracking.
  • Testing the pipeline for accuracy and consistency.

A reproducible ML pipeline is independently verifiable and ensures consistently accurate outcomes. It is a reliable pipeline that you can trust for high-performant ML models and precise results.

Unified Feature Store 

Use a unified feature store for both model training and production environments by:

  • Implementing a feature store solution, such as Qwak's MLOPs platform, that integrates with the rest of your pipeline to transform your data for training and serving.
  • Storing features in a consistent format.
  • Using the same features for a model's training and inference.

A centralized feature repository ensures feature consistency and provides high-quality training and production data. Additionally, it promotes feature reuse that can accelerate your model development process and helps you scale your pipeline as your data grows.

How does a unified feature store impact ML pipeline performance? 

A feature store is a centralized repository that houses all features used in machine learning models. A unified feature store refers to integrating a feature store into an end-to-end ML pipeline, equally serving the training and production layers of the pipeline and providing seamless access to consistent features for training, evaluation, and deployment. It can significantly improve the performance of your ML pipeline in the following ways:

  • Centralized Data Management: With a unified feature store, the data management is centralized, and the features are stored in a consistent format, reducing the time and effort required to prepare and pre-process the data.
  • Feature Reuse: Reusing features in multiple models improve your pipeline agility by quickly developing and deploying new models without compromising on model performance.
  • Faster Model Training: Pre-computed features enable you to skip data preparation and jump straight to model production and experimentation.¬†
  • High-Performance Models: A unified feature store ensures the development of successful models by providing high-performance features that you can optimize for different models.
  • Improved Collaboration: A unified feature store is a central point for data scientists and engineers to access features. The features are also well-documented, so both teams can promptly share features and build upon the work of other members, improving the collaboration between team members.¬†

Build scalable ML pipelines with Qwak’s ML engineering platform 

Building robust and scalable machine learning pipelines is a critical aspect of machine learning operations (MLOps). So, in addition to following best practices for implementing ML pipelines, you need the right tool to help you manage the complete lifecycle of a machine learning model from development to production.

Qwak is a fully managed MLOps platform that unifies ML engineering and data operations. It provides an agile infrastructure that enables the continuous productionization of ML models at scale. Qwak is your one-stop solution to build robust ML pipelines with its built-in ML services, including:

So what are you waiting for? To streamline your ML production with scalable ML pipelines, get started today for free and leverage Qwak's all-in-one ML solution to increase your ML outputs.

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