Foundation Models and Customization
Description
Comprehensive understanding of foundation model architectures, customization techniques including fine-tuning, prompt engineering, and model optimization for specific use cases and domains.
Learning Objectives
Learners will master foundation model concepts, architecture design considerations, customization methodologies including fine-tuning and parameter-efficient tuning, prompt engineering techniques, model evaluation criteria, and deployment strategies. They will understand how to select, customize, and optimize foundation models for specific business applications.
Topics (11)
Comprehensive coverage of evaluation frameworks, benchmark suites, performance metrics, and comparative analysis methods.
Study of model compression techniques, inference optimization, hardware acceleration, and efficiency improvements.
End-to-end deployment considerations, infrastructure requirements, monitoring systems, and production best practices.
Comprehensive study of foundation model architectures, design patterns, multi-modal capabilities, and architectural trade-offs.
Detailed study of model evaluation frameworks, benchmarking methodologies, performance metrics, and selection criteria for different applications.
Introduction to fine-tuning methodology, transfer learning principles, and the theoretical foundations of model adaptation.
Comprehensive coverage of full fine-tuning methodologies, training procedures, data requirements, and optimization strategies.
Study of PEFT techniques, low-rank adaptation, adapter modules, prompt tuning, and efficient training strategies.
Specialized approaches for adapting foundation models to specific domains, industry requirements, and regulatory constraints.
Comprehensive study of prompt design, prompt optimization, few-shot learning, and prompt engineering best practices.
Advanced prompting strategies, reasoning techniques, prompt chaining, and complex problem-solving approaches.