diff --git a/mlworkflow.qmd b/mlworkflow.qmd
index 93479bce6..518a41c52 100644
--- a/mlworkflow.qmd
+++ b/mlworkflow.qmd
@@ -1,6 +1,10 @@
 # ML Workflow
 
-In this chapter, we're going to learn about the machine learning workflow. The ML workflow is a systematic and structured approach that guides professionals and researchers in developing, deploying, and maintaining ML models. This workflow is generally delineated into several critical stages, each contributing towards the effective development of intelligent systems. Here's a broad outline of the stages involved:
+In this chapter, we're going to learn about the machine learning workflow. It will set the stages for the later chapters that dive into the details. But to prevent ourselves from missing the forest for the trees, this chapter gives a high level overview of the stpes involved in the ML workflow. 
+
+The ML workflow is a systematic and structured approach that guides professionals and researchers in developing, deploying, and maintaining ML models. This workflow is generally delineated into several critical stages, each contributing towards the effective development of intelligent systems. 
+
+Here's a broad outline of the stages involved:
 
 ## Overview
 
@@ -45,7 +49,7 @@ Now, let's explore these differences in detail:
 
 ## Roles \& Responsibilities
 
-Creating a machine learning solution, particularly for embedded AI systems, is a multidisciplinary endeavor involving various experts and specialists. Here is a list of personnel that are typically involved in the process, along with brief descriptions of their roles:
+As we work through the various tasks at hand, you will realize that there is a lot of complexity. Creating a machine learning solution, particularly for embedded AI systems, is a multidisciplinary endeavor involving various experts and specialists. Here is a list of personnel that are typically involved in the process, along with brief descriptions of their roles:
 
 **Project Manager:**
 
@@ -113,6 +117,8 @@ Creating a machine learning solution, particularly for embedded AI systems, is a
 - Focus on ensuring the security of the AI system.
 - Work on identifying and mitigating potential security vulnerabilities.
 
+Don't worry! You don't have to be a one-stop ninja.
+
 Understanding the diversified roles and responsibilities is paramount in the journey to building a successful machine learning project. As we traverse the upcoming chapters, we will wear the different hats, embracing the essence and expertise of each role described herein. This immersive method nurtures a deep-seated appreciation for the inherent complexities, thereby facilitating an encompassing grasp of the multifaceted dynamics of embedded AI projects.
 
 Moreover, this well-rounded insight promotes not only seamless collaboration and unified efforts but also fosters an environment ripe for innovation. It enables us to identify areas where cross-disciplinary insights might foster novel thoughts, nurturing ideas and ushering in breakthroughs in the field. Additionally, being aware of the intricacies of each role allows us to anticipate potential obstacles and strategize effectively, guiding the project towards triumph with foresight and detailed understanding.