Group 1 - AI development is a complex system engineering involving multiple disciplines and stages, following a closed-loop logic from demand analysis to continuous optimization [1] - Demand analysis is fundamental to AI development, requiring clear definition of core indicators such as accuracy and response time, as well as application scenarios and user groups [2] - Feasibility assessment must cover technical, data, and cost aspects, evaluating algorithm maturity, data volume and quality, and hardware and labor costs [2] Group 2 - Data is considered the "fuel" for AI systems, necessitating the collection of thousands to millions of labeled images from various sources [4] - Data management includes cleaning, labeling, and dividing data into training, validation, and test sets to ensure objective model evaluation [4] Group 3 - Model development involves selecting appropriate architectures based on problem types and data characteristics, with common use of CNN for image recognition and RNN or Transformer for sequential data [5] - Monitoring loss function curves during training is essential to prevent overfitting or underfitting, with performance evaluated using metrics like accuracy and recall [5] Group 4 - Model deployment must consider hardware environments and system integration, with various deployment options available to meet different enterprise needs [6] - Continuous iteration is necessary, incorporating user feedback and data drift detection to adapt models to new diseases or equipment upgrades [6] Group 5 - The essence of AI development lies in the dynamic balance between technology, business, and data, requiring developers to find optimal solutions among precision, quality, efficiency, and cost [7]