Home > AI Solutions > Artificial Intelligence > White Papers > Training Models Made Easy with Dell Enterprise Hub > Solution approach
As a key innovation in AI, training models have several distinct advantages over regular models. Training models work by enabling users to use their own custom data to train a model. The model is still trained using the parameters that the developers initially provided, but custom data is added to specialize the model further. This gives the model a more specific training focus on the custom data provided, which can increase performance and efficiency. Also, models can be re-trained, which means that enterprises can continuously use the most up-to-date data for training the AI model. The ability to enhance the specialization and knowledge of an AI model is a powerful asset that enterprises can leverage to improve their uses of generative AI.
In this white paper and the blog Using Synthetic Data Generation to Fine Tune a model from the Dell Enterprise Hub | Dell Technologies Info Hub, Dell Technologies provides details about the implementation and use of training models. This paper provides a high-level overview of the deployment and training process, while the blog provides a deeper dive into the individual steps involved in fine-tuning and deploying the trained model.
The following workflow diagram shows the relationship between Dell Enterprise Hub and the training data: