Updated the frameworks section outline

_quarto.yml

@@ -34,10 +34,10 @@ book:
     - embedded_ml.qmd
     - workflow.qmd
     - data_engineering.qmd
+    - frameworks.qmd
     - training.qmd
     - ondevice_ai.qmd
     - optimizations.qmd
-    - frameworks.qmd
     - hw_acceleration.qmd
     - benchmarking.qmd
     - ondevice_learning.qmd

frameworks.qmd

@@ -5,20 +5,53 @@
 Explanation: Discuss what ML frameworks are and why they are important. Also, elaborate on the aspects involved in understanding how an ML framework is developed and deployed.
 
 - Definition of ML Frameworks
-- What is an embedded ML framework?
-- Why are embedded ML frameworks important?
-- Challenges of embedded ML
-- Benefits of using embedded ML frameworks, trade-offs, and differences.
+- What is an ML framework?
+- Why are ML frameworks important?
+- Go over the design and implementation
+- Examples of ML frameworks
+- Challenges of embedded systems
 
-## Typical ML Frameworks
+## Evolution of AI Frameworks
+
+- High-level vs. low-level frameworks
+- Static vs. dynamic computation graph frameworks
+- Plot showing number of different frameworks and shrinking
+
+## Types of AI Frameworks
+
+- Cloud-based AI frameworks
+- Edge AI frameworks
+- TinyML frameworks
+
+## Popular AI Frameworks
 
 Explanation: Discuss the most common types of ML frameworks available and provide a high-level overview, so that we can set into motion what makes embedded ML frameworks unique.
 
 - TensorFlow, PyTorch, Keras, ONNX Runtime, Scikit-learn
 - Key Features and Advantages
 - API and Programming Paradigms
+- Table comparing the different frameworks
+
+## Basic Components
+
+- Computational graphs
+- Tensor data structures
+- Distributed training
+- Model optimizations
+- Code generation
+- Differentiable programming
+- Hardware acceleration support (GPUs, TPUs)
+
+## Advanced Features
+
+- AutoML, No-Code/Low-Code ML
+- Transfer learning
+- Federated learning
+- Model conversion
+- Distributed training
+- End-to-End ML Platforms
 
-## Constraints for Embedded AI
+## Embedded AI Constraints
 
 Explanation: Describe the constraints of embedded systems, referring to the previous chapters, and remind readers about the challenges and why we need to consider creating lean and efficient solutions.
 
@@ -46,26 +79,33 @@ Explanation: Now, discuss specifically about the unique embedded AI frameworks t
 - Edge Impulse
 - Others (briefly mention some less common but significant frameworks)
 
+## Choosing the Right Framework
+
+- Factors to consider: ease of use, community support, performance, scalability, etc.
+- Integration with data engineering tools
+- Integration with model optimization tools
+
 ## Framework Comparison
 
 Explanation: Provide a high-level comparison of the different frameworks based on class slides, etc.
 
 - Table of differences and similarities
 
-## Toolchain Integration
-
-Explanation: Help people understand that it's more than just the framework, and that elements need to fit into the ecosystem of various aspects that exist in an embedded system.
-
-- Compatibility with Embedded Development Environments
-- Integration with Firmware and Hardware
-
 ## Trends in ML Frameworks
 
 Explanation: Discuss where these ML frameworks are heading in the future. Perhaps consider discussing ML for ML frameworks?
 
 - Framework Developments on the Horizon
 - Anticipated Innovations in the Field
 
+## Challenges and Limitations
+
+Explanation: None of the frameworks are perfect, so it is important to understand their limitations and challenges.
+
+- Model compatibility and interoperability issues
+- Scalability and performance challenges
+- Addressing the evolving needs of AI developers
+
 ## Conclusion
 
 - Summary of Key Takeaways