General -> Traditional ML Workflow

workflow.qmd

@@ -11,7 +11,7 @@ The ML workflow is a structured approach that guides professionals and researche
 
 * Understand the ML workflow and gain insights into the structured approach and stages involved in developing, deploying, and maintaining machine learning models.
 
-* Learn about the unique challenges and distinctions between workflows for general machine learning and embedded AI.
+* Learn about the unique challenges and distinctions between workflows for Traditional machine learning and embedded AI.
 
 * Appreciate the various roles involved in ML projects and understand their respective responsibilities and significance.
 
@@ -42,24 +42,24 @@ The ML workflow is iterative, requiring ongoing monitoring and potential adjustm
 * **Testing**: Rigorously test the workflow to ensure its functionality.
 * **Security**: Safeguard your workflow and data, particularly when deploying models in production settings.
 
-## General vs. Embedded AI
+## Traditional vs. Embedded AI
 
 The ML workflow serves as a universal guide, applicable across various platforms including cloud-based solutions, edge computing, and tinyML. However, the workflow for Embedded AI introduces unique complexities and challenges, which not only make it a captivating domain but also pave the way for remarkable innovations.
 
 ### Resource Optimization
-- **General ML Workflow**: Prioritizes model accuracy and performance, often leveraging abundant computational resources in cloud or data center environments.
+- **Traditional ML Workflow**: Prioritizes model accuracy and performance, often leveraging abundant computational resources in cloud or data center environments.
 - **Embedded AI Workflow**: Requires careful planning to optimize model size and computational demands, given the resource constraints of embedded systems. Techniques like model quantization and pruning are crucial.
 
 ### Real-time Processing
-- **General ML Workflow**: Less emphasis on real-time processing, often relying on batch data processing.
+- **Traditional ML Workflow**: Less emphasis on real-time processing, often relying on batch data processing.
 - **Embedded AI Workflow**: Prioritizes real-time data processing, making low latency and quick execution essential, especially in applications like autonomous vehicles and industrial automation.
 
 ### Data Management and Privacy
-- **General ML Workflow**: Processes data in centralized locations, often necessitating extensive data transfer and focusing on data security during transit and storage.
+- **Traditional ML Workflow**: Processes data in centralized locations, often necessitating extensive data transfer and focusing on data security during transit and storage.
 - **Embedded AI Workflow**: Leverages edge computing to process data closer to its source, reducing data transmission and enhancing privacy through data localization.
 
 ### Hardware-Software Integration
-- **General ML Workflow**: Typically operates on general-purpose hardware, with software development occurring somewhat independently.
+- **Traditional ML Workflow**: Typically operates on general-purpose hardware, with software development occurring somewhat independently.
 - **Embedded AI Workflow**: Involves a more integrated approach to hardware and software development, often incorporating custom chips or hardware accelerators to achieve optimal performance.
 
 ## Roles & Responsibilities