5 Tips to Improve the Efficiency of Your Model Training

In order to make an ethical machine learning model, developers should practice responsible AI and keep their models as efficient and useful as possible. Find some tips here.

Machine learning has a lot of applications nowadays, and with the explosion of AI and apps like ChatGPT, the list of uses and its popularity has only grown. Every industry from marketing to fine art has an opinion. But people are worried that this new technology can be used for the wrong reasons. In order to make an ethical machine learning model, developers should practice responsible AI and keep their models as efficient and useful as possible. Take a look at our tips for optimising your model training.

What is model training?

Model training is essentially a phase of testing in the machine learning lifecycle. It's an important point in responsible AI where the developer tries to gauge the best combination of weights and biases to a machine learning algorithm in order to avoid loss of function or rising issues in the future.

Model training is important to the process, like any other testing stage in a STEM project, in order to keep function optimised. It's a real-time test as close to the real-world deployment as possible. So, it can be thought of as a troubleshooting stage. Model training allows your model to be validated, tested, and finally deployed to its function.

Model training your machine learning models will allow you to gain feedback, detect changes, and improve your model.

Treat health issues

Oftentimes, machine learning models can gain unnoticed health issues in the pipelines due to failures that are ingrained in automated steps. Model monitoring will allow you to spot health issues in otherwise automated processes, like failing deployment processes, and in turn, allow you to solve them.

Multiple algorithms

Like a lot of things in life, more is more. We can all agree that this is the case for education, so why would that stop at only a human's education? Machine learning improves with the amount of information you give it, so you might want to feed it various algorithms in order to give it a more fully rounded perspective and help it treat outlier issues that might arise in its function.

Add more data

Additionally, your machine learning model will thrive off of more data to better inform its algorithms. This allows the data to act as a source, telling for itself rather than relying on weak correlations or assumptions. If you can access more data for a real-world company project, do use it.

Feature engineering

Feature engineering allows the developer to better extract even more information from existing data, which can better explain the variance in the data, giving better model accuracy. You can look into feature transformation, for instance, to normally distribute data, or feature creation, which allows you to gain variables from existing variables to better understand the relationship between a data set.

Identify a skew

If your model is still not producing good results, there might be a skew in the system. The cause of this might be differences in the environment of the model and real-world application. You can go through the process in various environments to attempt to identify a skew in the process.


  • Do not include your name, "with regards" etc in the comment. Write detailed comment, relevant to the topic.
  • No HTML formatting and links to other web sites are allowed.
  • This is a strictly moderated site. Absolutely no spam allowed.
  • Name: