Summary

In this chapter, we delved into the world of XAI and its relevance in modern MLOps. We discussed how XAI aids in building trust, ensuring regulatory compliance, debugging and improving models, and addressing ethical considerations.

We explored different explanation techniques for various types of data, including tabular, image, and text data. Techniques such as LIME, SHAP, permutation feature importance, and others were discussed for tabular data. For image data, methods such as Integrated Gradients and XRAI were explained, while text-specific LIME was presented for text data.

This chapter also provided an overview of the XAI features available in GCP, including both feature-based and example-based explanations.

At this point, you should have gained a good understanding of XAI, its importance, various techniques, and practical applications in the context of Vertex AI. As the field of AI continues to evolve, the role of XAI in creating transparent, trustworthy, and...