Summary

In this chapter, we introduced a wide variety of use cases for foundation modeling, encompassing scenarios where you can fine-tune an existing foundation model and where pretraining itself is competitive. We provided a simple economics framework to help you make the case for your pretraining project, notably by tying it to how much you expect your business to increase based on a more accurate model. After that, we talked about evaluating your dataset, comparing it to research datasets, and learning how to think critically about its sampling mechanism. We set up some basic ideas to use this critical thinking for framing experiments, which we’ll continue in the next chapter. We learned about the scaling laws and presented an open source notebook you can use to find which dataset size will help you hit performance levels, given fixed model and compute budgets. We talked about detecting and mitigating bias in your datasets, along with enhancing these with augmentation, modalities...