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Over the last thirteen chapters, we have explored the field of Machine Learning (ML) interpretability. As stated in the preface, it's a broad area of research, most of which hasn't even left the lab and become widely used yet, and this book has no intention of covering absolutely all of it. Instead, the objective is to present various interpretability tools in sufficient depth to be useful as a starting point for beginners and even complement the knowledge of more advanced readers. This chapter will summarize what we've learned in the context of the ecosystem of ML interpretability methods, and then speculate on what's to come next!
These are the main topics we are going to cover in this chapter:
- Understanding the current landscape of ML interpretability
- Speculating on the future of ML interpretability
