What is a good practice for training a model with a small dataset in a Jupyter Notebook?

Training a model within the notebook instance is a good practice when working with a small dataset because it allows for immediate access to tools, resources, and libraries necessary for data analysis and model creation. In a Jupyter Notebook, you can experiment with your data interactively, adjusting parameters and visualizing results on the fly. The compact size of the dataset means that the computational demands are relatively low, making local execution viable and efficient for model training.

Additionally, Jupyter Notebooks support straightforward integration with visualization libraries, allowing you to examine model performance and dataset characteristics without the need for complex setups. This environment fosters an iterative workflow, which is particularly beneficial when fine-tuning models based on the small dataset, as it promotes quick changes and immediate feedback.

While utilizing the cloud environment, combining datasets, or relying on AutoML features could also be beneficial in different contexts, they might not directly address the unique advantages that come with directly working in a Jupyter Notebook for small datasets. Such practices may introduce unnecessary complexity or expenses that are not needed given the local context and requirements of small dataset training.