Updated the purpose

contents/core/frameworks/frameworks.qmd

@@ -10,9 +10,11 @@ Resources: [Slides](#sec-ai-frameworks-resource), [Videos](#sec-ai-frameworks-re
 
 ![_DALL·E 3 Prompt: Illustration in a rectangular format, designed for a professional textbook, where the content spans the entire width. The vibrant chart represents training and inference frameworks for ML. Icons for TensorFlow, Keras, PyTorch, ONNX, and TensorRT are spread out, filling the entire horizontal space, and aligned vertically. Each icon is accompanied by brief annotations detailing their features. The lively colors like blues, greens, and oranges highlight the icons and sections against a soft gradient background. The distinction between training and inference frameworks is accentuated through color-coded sections, with clean lines and modern typography maintaining clarity and focus._](images/png/cover_ml_frameworks.png)
 
-This chapter explores the landscape of AI frameworks that serve as the foundation for developing machine learning systems. AI frameworks provide the tools, libraries, and environments to design, train, and deploy machine learning models. We explore the evolutionary trajectory of these frameworks, dissect the workings of TensorFlow, and provide insights into the core components and advanced features that define these frameworks.
+## Purpose
 
-Furthermore, we investigate the specialization of frameworks tailored to specific needs, the emergence of frameworks specifically designed for embedded AI, and the criteria for selecting the most suitable framework for your project. This exploration will be rounded off by a glimpse into the future trends expected to shape the landscape of ML frameworks in the coming years.
+_What role do AI frameworks play in enabling scalable and efficient machine learning systems, and how do their features and capabilities influence the design, training, and deployment of models across diverse environments?_
+
+AI frameworks are the backbone of modern machine learning workflows, providing the tools and abstractions necessary to design, train, and deploy complex models across diverse environments. These frameworks bridge the gap between model development and system execution, allowing engineers to take models designed by developers and efficiently execute them on various underlying systems. By understanding the components, capabilities, and limitations of these frameworks, machine learning practitioners can make informed decisions about the tools that best enable scalability, efficiency, and adaptability. This knowledge prepares us to address the challenges of machine learning deployment, optimization, and system-level integration.
 
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