Updated purpose

contents/core/workflow/workflow.qmd

@@ -10,9 +10,11 @@ Resources: [Slides](#sec-ai-workflow-resource), [Videos](#sec-ai-workflow-resour
 
 ![_DALL·E 3 Prompt: Create a rectangular illustration of a stylized flowchart representing the AI workflow/pipeline. From left to right, depict the stages as follows: 'Data Collection' with a database icon, 'Data Preprocessing' with a filter icon, 'Model Design' with a brain icon, 'Training' with a weight icon, 'Evaluation' with a checkmark, and 'Deployment' with a rocket. Connect each stage with arrows to guide the viewer horizontally through the AI processes, emphasizing these steps' sequential and interconnected nature._](images/png/cover_ai_workflow.png)
 
-The ML workflow is a structured approach that guides professionals and researchers through developing, deploying, and maintaining ML models. This workflow is generally divided into several crucial stages, each contributing to the effective development of intelligent systems.
+## Purpose {.unnumbered}
 
-In this chapter, we will explore the machine learning workflow, setting the stage for subsequent chapters that go deeper into the specifics. This chapter focuses only presenting a high-level overview of the steps involved in the ML workflow.
+_AI sytems are more than just machine learning models, so how can we systematically and more holistically approach AI systems engineering?_
+
+This chapter highlights the broader scope of machine learning beyond understanding neural network model architectures. It introduces readers to a comprehensive AI workflow, encompassing stages from data gathering to operational deployment. The focus extends to critical factors such as resource management, data privacy, and integration processes. By laying this foundation, the chapter serves as a preview of the detailed engineering and operational concepts that follow, guiding both newcomers and seasoned practitioners toward a holistic understanding of AI systems in practice.
 
 ::: {.callout-tip}
 
@@ -36,6 +38,10 @@ In this chapter, we will explore the machine learning workflow, setting the stag
 
 ![Multi-step design methodology for the development of a machine learning model. Commonly referred to as the machine learning lifecycle](images/png/ML_life_cycle.png){#fig-ml-life-cycle}
 
+The ML workflow is a structured approach that guides professionals and researchers through developing, deploying, and maintaining ML models. This workflow is generally divided into several crucial stages, each contributing to the effective development of intelligent systems.
+
+In this chapter, we will explore the machine learning workflow, setting the stage for subsequent chapters that go deeper into the specifics. This chapter focuses only presenting a high-level overview of the steps involved in the ML workflow.
+
 @fig-ml-life-cycle illustrates the systematic workflow required for developing a successful machine learning model. This end-to-end process, commonly referred to as the machine learning lifecycle, enables you to build, deploy, and maintain models effectively. It typically involves the following key steps:
 
 1. **Problem Definition** - Start by clearly articulating the specific problem you want to solve. This focuses on your efforts during data collection and model building.