Summary

Beginning with an examination of AutoML frameworks, this chapter highlighted the value these systems bring to the entirety of the data science pipeline, facilitating each stage from data preparation to model deployment. We then considered how the integration of LLMs can further elevate productivity and make data science more approachable for both technical and non-technical stakeholders.

Diving into code generation, we saw parallels with software development, as discussed in Chapter 6, Developing Software with Generative AI, observing how tools and functions generated by LLMs can respond to queries or enhance datasets through augmentation techniques. This included leveraging third-party tools like WolframAlpha to add external data points to existing datasets. Our exploration then shifted toward the use of LLMs for data exploration, building upon the techniques for ingesting and analyzing voluminous textual data detailed in Chapter 4, Building Capable Assistants, on question...