Updated purpose

contents/core/data_engineering/data_engineering.qmd

@@ -10,7 +10,11 @@ Resources: [Slides](#sec-data-engineering-resource), [Videos](#sec-data-engineer
 
 ![_DALL·E 3 Prompt: Create a rectangular illustration visualizing the concept of data engineering. Include elements such as raw data sources, data processing pipelines, storage systems, and refined datasets. Show how raw data is transformed through cleaning, processing, and storage to become valuable information that can be analyzed and used for decision-making._](images/png/cover_data_engineering.png)
 
-Data is the lifeblood of AI systems. Without good data, even the most advanced machine-learning algorithms will not succeed. However, TinyML models operate on devices with limited processing power and memory. This section explores the intricacies of building high-quality datasets to fuel our AI models. Data engineering involves collecting, storing, processing, and managing data to train machine learning models.
+## Purpose {.unnumbered}
+
+_How does sourcing, preparation, and the quality of data influence the design, performance, and scalability of machine learning systems?_
+
+Data is the lifeblood of AI systems. This chapter explores the engineering aspects of data sourcing, processing, and quality assurance, highlighting their impact on the scalability, efficiency, and adaptability of machine learning workflows. Building on earlier discussions about neural network architectures, this chapter addresses the design challenges of creating reliable data pipelines that integrate seamlessly with machine learning systems. The insights gained here will equip readers with the skills to engineer data solutions that support optimization and deployment, paving the way for advanced applications in complex environments.
 
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