As the demand for machine learning is increasing in various fields like corporate offices, hospitals, server-side services, traffic, manufacturing, and many more fields, it is important to deliver the best solutions for any problem related to machine learning. This will alternatively increase the efficiency of machine learning models. The design patterns are a way in which the various stages and steps to train machine learning models are recorded so that it can be used to solve a problem with much efficiency and less error. In this article, we will see the various machine learning design patterns. But before that let us have a brief look over what are design patterns and why we need this.

What are Design Patterns?

In general terms, Design patterns in machine learning are defined as the best practice that can be taken to solve any kind of problem during the training of a machine learning model. It includes various kinds of approaches such as documentation of various stages of the training model, fixing any kind of error during the training, making checkpoints for a particular stage of the process, etc.

Need for Design Patterns in Machine Learning

Developing a machine learning project is not an easy task. There are various parameters such as scalability, performance, reliability, etc that need to be taken care of for making a good and efficient machine learning project. Machine learning models do not consist of only developing models with existing concepts, but it is a combination of existing concepts with new inventions in machine learning, which alternatively results in a brilliant solution for a specific problem. So, design patterns help in tracking the different crucial stages during the training of a machine learning model. This divides the various checkpoints and also saves the current process because training a machine learning model is a long and iterative process. Design patterns help to tackle all those mentioned problems.

Various Design Patterns for Machine Learning Projects

There are more than 25 design patterns in machine learning. Here we will discuss some of the most important and common design patterns that we need during the training of a machine learning model.

Rebalancing: Problem Representation Design Pattern

While dealing with problems like Fraud and Spam detection, the most common issue in performing the classification problem is “Imbalanced datasets”. Generally, most of the machine learning models consider that all the datasets are balanced which alternatively results in poor performance in providing the result. So, there are some steps that need to be taken to fix this issue of imbalanced datasets and get good results are as follows −

Performance Metric

We know that accuracy is the best metric in this case but it is not advised to use it in case of imbalanced datasets. In this case, the developer wants to maximize the precision of the result, so they use other metrics such as F1 score or AUC which results in more efficiency of the output of the machine learning models.

Sampling Methods

The sampling method is used to balance the imbalanced samples in a particular dataset. Given below are the two sampling techniques −

Over-Sampling − This sampling is used to balance the samples of the minority class. In this technique, the sample which does not add any value to the datasets are duplicated from the minor class.

Down-Sampling − In this technique, the sample from the minority class which may cause information loss is replaced by a sample from the majority class.

Weighted Classes

In this approach, we use the penalized learning algorithm. This increases the value of misclassification of the sample from the minority class.

Transform: Reproducibility Design Pattern

This design pattern is used to separate the input from features. Generally, most design patterns do not use raw input as features. But in this design pattern, machine learning models can be easily transformed into production by separating the various inputs, features, etc. A very popular platform for doing this task is Tensorflow. Here we can efficiently transform these data by using “tf.platform”.

Checkpoints: Model Training Design Pattern

A checkpoint is defined as an internal stage during the development of the training model. From this particular checkpoint, any task can be resumed if the training stops due to any reason such as power-cot, faults in the operating system, task, preemption, etc. The checkpoints consist of data such as the model’s weight, the current rate of learning, etc, Always keep a checkpoint at the end of the process and always try to keep the checkpoint when the model is at best accuracy.

Workflow Pipeline: Reproducibility DesignPattern

In this design pattern, we try to isolate each separate step that s involved in the training of the machine learning model. This helps to maintain scalability during the entire training process and also it organizes the various tasks into separate sections. A machine learning task is done in many iterative steps, which continue till the finalization f the project. So, making separate isolated tasks makes it easy to track any small change in the model. Here the concept of MLOps is introduced, whose aim is continuous integration and delivery.

Explainable Predictions: Responsible A.I. Design Pattern

Machine learning models are generally considered black boxes. One must have clear and good knowledge of model behaviour while training a machine learning model. This makes the developer easily catch and diagnose any kind of error in the model. Finding any error makes it possible to decide whether that particular object will be further employed in the model or not. This results in introducing the explainability of a model which is a major factor for an ideal and responsible artificial intelligence system.

Conclusion

• Design patterns in machine learning are defined as the best practice that can be taken to solve any kind of problem during the training of a machine learning model.

• Some important design patterns for machine learning are Rebalancing, Transforming, Checkpoints, Workflow Pipelines, and Explainable Predictions.

• The sampling technique is used to balance the imbalanced samples in a particular dataset.

• Two types of sampling methods are Over-sampling and Down-sampling.

• Machine learning models can be easily transformed into production by separating the various inputs, features, etc with the help of the TenserFlow platform.

• A checkpoint is defined as an internal stage during the development of the training model.