This presentation builds on the "Vectorization in MLIR" talk delivered at LLVM Dev Meeting 2023, delving deeper into the Linalg Vectorizer's capabilities within MLIR. The Linalg Vectorizer combines a simple tiling and basic-block vectorization approach with advanced concepts such as vector masking or support for scalable vectors. In this follow-up session, we will explore the implementation details of the Linalg Vectorizer, focusing on how it handles Linalg operations beyond linalg.matmul, which was covered in the previous talk. We'll also compare various vectorization pre-processing strategies—such as masking, peeling, and padding—and demonstrate how to effectively apply these strategies. Additionally, we will address the unique challenges posed by scalable vectors, including our approach to extending value-bounds analysis to accommodate these complexities. Specifically, we'll provide an update on the ongoing support for the Scalable Matrix Extension (SME), a CPU extension that enables 2D scalable vectors.