Supportsoft Glossary

Training Data Preparation for Reliable AI Models

To create Artificial Intelligence (AI) and Machine Learning (ML) applications, we need to collect and organise training data. The responses provided to the training model will be based on the data that was supplied to it during the training process. The datasets created for the purposes of model training will ultimately determine how successful a particular model will be in making accurate decisions and predictions.

In order for ML algorithms and models to perform effectively, the training dataset should include high-quality, diverse and representative samples of data from actual interactions with users. Any time inaccurate or biassed data has been included in the training dataset, the ML algorithms will provide inaccurate predictions and potentially harmful outcomes. There are several steps in the preparation of a dataset for use with a machine learning algorithm, including the cleansing, labelling, and validating.

The importance of data preparation in the deployment of an AI solution has been emphasised in the IT and ITES industries. Effective data management helps create a performant model while lowering the likelihood of producing errors in the production setting.

Organisations need to keep up with changes in the business environment through maintaining and updating their training data. Continuous improvements will help ensure that all machine learning models continue to be accurate and relevant.

Investing in the right training data enables organisations to create AI systems and applications capable of providing accurate and reliable outcomes consistent with one another.