A Simple Framework for Building an End-to-End Machine Learning Pipeline
Machine learning has become an important technique for solving complex problems in today’s data-driven world. It enables users and businesses to analyse data and make predictions with unparalleled accuracy.
To build a robust model, machine learning practitioners apply a series of steps from data collection to model evaluation. In this post, we’ll dive into those steps and walk through a simple framework to understand how these steps create an end-to-end machine learning pipeline.
What is a machine learning pipeline?
A machine learning pipeline is a systematic way of moving from raw data to insights and predictions. It’s not just about creating a model; it’s about streamlining the entire process from data ingestion to actionable outputs. This cohesive approach ensures reliability and consistency in machine learning projects.
The nine-step framework
Building a machine learning model is analogous to building a house: with a solid foundation and meticulous planning, the final structure can stand the test of time. A structured framework ensures that we not only create an effective model, but also make the entire process reproducible and scalable.
The nine-step framework below serves as a blueprint for data scientists and machine learning engineers working within the pipeline, guiding them from the ideation phase to a production-ready solution.
Flowchart for the machine learning pipeline
1. Problem formulation
This step forms the bedrock of the machine learning journey; it is essential to ask the appropriate questions so that the eventual model addresses the business objectives. These questions include:
What business goals are you trying to achieve?
Are you predicting a numerical value, classifying data or clustering it?
What are the right metrics to evaluate the model performance?
Your answers will guide your choice of model and techniques. Furthermore, defining the business or performance metrics upfront—be it accuracy, precision, recall or any other—sets clear expectations and evaluation standards.
2. Data collection and labelling
Think of data as the raw material for your model. Data collection involves understanding the source of your data, ensuring its quality and gathering more if required.
If you’re venturing into supervised learning, accurate data labelling becomes paramount. At this point, consider asking a domain expert to insert or inspect the data labels.
3. Data exploration
Before diving into data pre-processing, it’s crucial to explore and understand your data. Data exploration includes:
Visualising the data distributions to check for outliers and imbalanced data.
Identifying correlations between features using a statistical method known as the Pearson correlation coefficient. The purpose is to ensure that the features are correlated with the target variable and uncorrelated among themselves.
Checking for data biases to avoid skewed predictions.
Data exploration provides valuable insights to inform subsequent stages, such as feature engineering and model training.
4. Data pre-processing / Feature engineering
This step is where you shape and mould your data. Data pre-processing and feature engineering can often make or break a model; by selecting and engineering features, you can leverage the data’s underlying patterns more effectively. This step involves:
Data cleaning: Handling missing values and removing outliers.
Feature selection: Selecting appropriate features and eliminating irrelevant ones.
Feature engineering: Combining or creating new features from existing ones.
Standardisation / Normalisation: Converting feature values into a similar scale.
Data segregation: Splitting the data into training, validation and testing sets.
5. Model training
With a refined dataset in hand, the actual ‘learning’ begins. Based on the problem at hand—regression, classification, clustering or any other—choose one or more appropriate algorithms. Train the models using your dataset, ensuring they learn the correct patterns and not overfitting based on the training dataset.
6. Model evaluation and selection
Using the metrics defined in Step 1, evaluate the models’ performance on an unseen dataset. This step often involves comparing multiple models to choose the optimal one for your problem. If a model is unable to meet the desired evaluation standard and business output, you may consider a different model or review the data pre-processing step to see how to improve the model performance.
7. Model tuning
Even the best models can often benefit from some fine-tuning. You can adjust the selected model’s hyperparameters using techniques like grid or random search. The goal here is to squeeze out that extra bit of model performance. However, remember that this step is optional and can be revisited later.
8. Model deployment
Transitioning from a model that works well in a controlled environment to one that thrives in the real world. Deployment can vary from a manual, script-driven process on your local system to an automated, cloud-based solution accessible via APIs (Application Programming Interfaces).
9. Monitor and iterate
The world is dynamic, and so is data. Post-deployment, it’s crucial to keep an eye on the model’s performance. Set up monitoring tools to catch any drift in model performance. If the model starts to underperform, it might be time to retrain or iterate on it with new data.
Automating the pipeline
As the field continues to advance, there are now many tools and platforms that allow for the automation of steps within the machine learning pipeline. Automated pipelines can significantly reduce the time and effort needed, allowing for automatic model updates and monitoring.
Building an automated machine learning pipeline can be a complex process, and it’s a discussion for another day. In the meantime, for a clearer understanding of the difference between a manual and automated pipeline, you can check out this Valohai article.
Conclusion
Building an end-to-end machine learning pipeline might sound daunting, but with a systematic approach, it becomes manageable and effective. Whether you’re a seasoned machine learning engineer or a budding data scientist, following this nine-step framework will guide you through the journey from problem formulation to model deployment and monitoring. And remember, while this framework provides a foundation, the nuances of each project will always require you to adapt to the project.
Check out my Project Hospitality notebook for an example of how to implement the machine learning pipeline framework.
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