Guidelines and best practices
While customizing a model, it’s ideal to consider the following practices for optimal results:
- Providing the dataset: The most important thing in ML is the dataset. Most of the time, how your model performs depends on the dataset you provide to train the model. So, providing quality data that’s aligned with your use case is important. If you’ve studied ML in university or worked in this field, you might have learned about various feature engineering and data processing techniques you can use to clean and process the data. For example, you can handle missing values in the dataset, make sure you don’t provide biased data, or ensure that the dataset follows the format that the model expects. If you would like to learn more about providing quality data, please read Feature Engineering for Machine Learning by Alice Zheng and Amanda Casari. This same principle applies to generative AI since it is essentially a subset of ML...
