Just some commented text as food for thought

data_engineering.qmd

@@ -1,5 +1,8 @@
 # Data Engineering
 
+<!--
+
+
 ## Introduction
 
 [//]: # Explanation: This section establishes the groundwork, defining data engineering and explaining its importance and role in Embedded AI. A well-rounded introduction will help in establishing the foundation for the readers.
@@ -8,6 +11,12 @@
 - Role of Data Engineering in Embedded AI
 - Synergy with Machine Learning and Deep Learning
 
+## Problem Definition
+- Identifying the Problem
+- Setting Clear Objectives
+- Benchmarks for Success
+- Stakeholder Engagement and Understanding
+
 ## Data Sourcing
 
 [//]: # Explanation: This section delves into the first step in data engineering - gathering data. Understanding various data types and sources is vital for developing robust AI systems, especially in the context of embedded systems where resources might be limited.
@@ -46,9 +55,10 @@
 
 [//]: # Explanation: Feature engineering involves selecting and transforming variables to improve the performance of AI models. It's vital in embedded AI systems where computational resources are limited, and optimized feature sets can significantly improve performance.
 
-- Feature Selection 
-- Feature Transformation 
-- Feature Scaling 
+- Importance of Feature Engineering 
+- Techniques of Feature Selection 
+- Feature Transformation for Embedded Systems
+- Real-time Feature Engineering in Embedded Systems
 
 ## Data Labeling
 
@@ -80,13 +90,8 @@
 - Data Security and Privacy 
 - Data Bias and Representativity 
 
-## Case Studies
-
-[//]: # Explanation: Case studies offer practical insights and lessons learned from real-world implementations, providing readers with a grounded understanding of the application of data engineering principles in embedded AI.
-
-- Real-World Examples of Data Engineering in Embedded AI 
-- Lessons Learned 
-
 ## Conclusion
 - The Future of Data Engineering in Embedded AI 
-- Key Takeaways
+- Key Takeaways
+
+-->

deployment.qmd

@@ -1,3 +1,11 @@
 # Deployment
 
+<!--
 
+## Evaluation and Testing
+- Evaluation Metrics 
+- Testing Strategies 
+- Performance Benchmarks in Embedded Systems 
+- Integration and End-to-End Testing 
+
+-->

mlops.qmd

@@ -1 +1,11 @@
 # MLOps
+
+<!--
+
+## Monitoring and Maintenance
+- Monitoring Strategies 
+- Maintenance Considerations 
+- Ensuring Sustained Performance in Embedded Systems 
+- Strategies for Remote Monitoring and Maintenance
+
+-->

training.qmd

@@ -1,33 +1,17 @@
 # Model Training
 
-## Selecting a Training Dataset
+<!--
 
-## Neural Network Architectures
+## Model Selection and Development
+- Overview of ML Models 
+- Criteria for Model Selection 
+- Model Development Considerations in Embedded Systems
+- Scalability and Resource Optimization
 
-### Multilayer Perceptron (MLP)
-
-### Convolutional Neural Networks
-
-### Recurrent Neural Networks
-
-### Transformers
-
-## Back Propagation
-
-## Convergence
-
-## Overfitting and Underfitting
-
-## Hyperparameters
-
-### Epochs
-### Learning Rate
-
-## Transfer Learning
-
-### Optimizer
-
-## Summary
-
-## Quiz
+## Hyperparameter Tuning
+- Understanding Hyperparameters 
+- Techniques for Hyperparameter Tuning 
+- Tuning for Embedded Systems 
+- Grid Search and Randomized Search Methods
 
+-->