diff --git a/contents/ml_systems/ml_systems.qmd b/contents/ml_systems/ml_systems.qmd
index fa8cccae..6751d8ed 100644
--- a/contents/ml_systems/ml_systems.qmd
+++ b/contents/ml_systems/ml_systems.qmd
@@ -30,7 +30,7 @@ As we journey further into this chapter, we will demystify the intricate yet cap
 
 ## Machine Learning Systems
 
-ML is rapidly evolving, with new paradigms emerging that are reshaping how these algorithms are developed, trained, and deployed. In particular, the area of embedded machine learning is experiencing significant innovation, driven by the proliferation of smart sensors, edge devices, and microcontrollers. This chapter explores the landscape of embedded machine learning, covering the key approaches of Cloud ML, Edge ML, and TinyML (@fig-cloud-edge-tinyml-comparison).
+ML is rapidly evolving, with new paradigms emerging that are reshaping how models are developed, trained, and deployed. In particular, the area of embedded machine learning is experiencing significant innovation, driven by the proliferation of smart sensors, edge devices, and microcontrollers. This chapter explores the landscape of embedded machine learning, covering the key approaches of Cloud ML, Edge ML, and TinyML (@fig-cloud-edge-tinyml-comparison).
 
 ![Cloud vs. Edge vs. TinyML: The Spectrum of Distributed Intelligence](images/png/cloud-edge-tiny.png){#fig-cloud-edge-tinyml-comparison}