Machine Learning is all over the place; therefore, it is important to capture best practices and solutions to solve common ML problems. One of the simplest ways to catch these problems and provide answers is to design patterns.
“Design patterns” are the best practices used by programmers to solve common problems while designing a system or an application. Below are a few design patterns that you must try:
Problem Representation Design Pattern: Rebalancing
The common scenario in classification problems like Fraud Detection, Spam Detection, or Anomaly Detection is Imbalanced datasets. But typical Machine Learning models for such classification work by assuming that all the classes are balanced and result in poor predictive performance. Some of the common strategies to handle imbalanced datasets are as follows:
- Choose Right Performance Metric: AUC or F1 scores are efficient for performance evaluation as the goal is to maximize precision and recall.
- Sampling Methods: Resampling is widely adopted to balance the dataset classes’ samples.
- Weighted Classes: It involves penalized learning algorithms to increase the misclassification cost in the minority class.
Reproducibility Design Pattern: Transform
The idea of this reproducibility design pattern is to separate input from features. You have to extra features from raw input to train a model. But in most ML problems, you can not use input directly as feature. Instead, you must apply various transformations like scaling, standardization, encoding, and others to reproduce them at the prediction time. So, it is vital to separate the inputs from the features, encapsulate the preprocessing steps, and include them into the model to ensure reproducibility.
Model Training Design Pattern: Checkpoints
The major attributes of a scalable system are resilience and fault tolerance. The snapshot of the model’s internal state is the checkpoint. So, you can resume training from this state to another later. However, during the activity, power outages, task preemptions, OS faults, and other unforeseen errors can happen, resulting in time and resource loss.
Reproducibility Design Pattern: Workflow Pipeline
The goal of the design pattern is to isolate and containerize the individual steps of a machine learning workflow into an organized workflow. It ensures scalability and maintainability. Generally, the machine learning development workflow is generally monolithic, containing a series of tasks from data collection to model training and evaluation. But the machine learning tasks are iterative.
Tracking the small changes in the workflow during the development process becomes complicated as the process iterates many times. It introduces the concept of MLOps, similar to DevOps concepts like continuous integration and continuous delivery. The key difference between MLOps and DevOps is that it is not only the code. It is also the data that must be continuously tested and validated in MLOps.
Responsible AI Design Pattern: Explainable Predictions
Generally, machine learning models are black boxes. But it is important to clearly understand the model behavior to diagnose the errors and identify potential biases. The major factor in Responsible Artificial Intelligence is introducing explainability in machine learning. Hence, the key idea is to interpret the machine learning models to understand why and how the model made the predictions in a certain way.
You can implement the above-mentioned techniques in most machine learning practices, but defining design patterns helps to create general reusable solutions for common problems. In addition, they help to communicate with engineers and solve problems by providing off-the-shelf solutions.