3 best practices in building deep learning models

Deep learning, also referred to as hierarchical learning, is a branch of machine learning. Building Deep Learning models involve designing a particular set of algorithms, which attempt to model high-level abstractions in data.

In the financial services industry, usage of Deep Learning models is becoming popular as they facilitate more accurate predictive analytics, which has helped in improving forecasting, recommendations, and risk analysis. Facebook also has just developed a Bot store to help businesses take advantage of chat bots based on Deep Learning models that can act as customer service representatives for sales or with simple troubleshooting. As the use of Deep Learning models is gaining momentum, several businesses are also facing challenges while building Deep Learning models.

Let’s discuss the three best practices in building Deep Learning models:

Accurate Definition of Performance Requirements

You need to clearly specify what constitutes good and bad performance. Performance requirements can constitute of accuracy or false positives or any other metrics that are important to the business. After defining these metrics, use the current model you have developed as baseline numbers. These numbers may be increased over a period of time as you improve the system. Defining performance requirements is necessary as, without them, you will not be able to setup the tests for determining if the system is behaving as expected. You must not proceed ahead until you have agreed upon minimum, mean or a performance range expectation.

Separate Prediction Algorithm from Model Coefficients

You may have used a library to create your Deep Learning predictive model. You can choose to deploy your model using that library or you can re-implement the predictive aspect of the model in your software. You might even want to set up your model as a web service. It is a good practice to separate the algorithm that makes predictions based on Deep Learning algorithms from the model internals.

Take a look at how to separate prediction algorithm from Model coefficients

• Select or Implement the Prediction Algorithm

Often the complexity of Deep Learning algorithms is in the model training, and not in making predictions. For example, making predictions with a regression algorithm is quite straightforward and easy to implement. But, if you are deciding to use the library to make predictions, get familiar with the API and with the dependencies. Implement these prediction algorithms properly, write unit tests, and make it robust.

• Serialize Your Model Coefficients

The numbers or structures learned by the Deep Learning model are known as coefficients. This data should be treated as software configuration. You must store it in an external file with the software project, create different versions and treat this configuration data like code as it can just as easily break your project. You might need to update this configuration in the future as you improve your model.

Develop Automated Tests for Building Deep Learning Models

You are required to build automated tests to prove that your Deep Learning model works as you expect. These tests are also known as regression tests. These tests ensure that your Deep Learning system has not regressed in its behaviour in the future as we make changes to different parts of the system.

Write regression tests for your model based on following guidelines:

• Start by collecting a small sample of data on which to make predictions.
• Use the production algorithm code and configuration for making predictions.
• Confirm results are expected in the test.

These tests are your early warning alarm. If they fail, your model can be broken and you can’t release the software or features that use the model. Ensure that these tests are strictly enforced upon the minimum performance requirements of the model.

As Deep Learning algorithms become increasingly prevalent across different industries, they are also becoming more accessible to other people who do not belong to the field of mathematics, engineering or robotics. Implementing these best practices will ensure successful deployment of your Deep Learning model.