Generative AI Model Selection Fundamentals
Master the essentials of generative AI model selection, deployment options, and performance optimization in this practical beginner-friendly course.
Master the essentials of generative AI model selection, deployment options, and performance optimization in this practical beginner-friendly course.
This comprehensive course provides a practical introduction to generative AI model selection and optimization. Students learn to understand AI model architecture, compare deployment options, and evaluate performance using benchmarks. The curriculum covers essential topics from basic model architecture to advanced concepts like Retrieval Augmented Generation (RAG). Through hands-on exercises, learners develop skills in model assessment, troubleshooting, and performance optimization. The course emphasizes practical application, ensuring students can make informed decisions about AI model selection for real-world projects.
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What you'll learn
Understand basic architecture of generative AI models
Compare different AI model deployment options
Evaluate models using benchmarks and custom assessments
Implement performance optimization techniques
Master in-context learning and RAG strategies
Troubleshoot and improve model performance
Skills you'll gain
This course includes:
71 Minutes PreRecorded video
2 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This practical course explores the fundamentals of generative AI model selection and optimization across four comprehensive modules. Students learn about AI model architecture, deployment options, and evaluation techniques. The curriculum covers essential topics including tokenization, semantic spaces, deployment strategies, benchmarking, and performance optimization. Special emphasis is placed on practical skills like creating custom benchmarks, implementing RAG, and troubleshooting model performance. The course combines theoretical knowledge with hands-on application to prepare learners for real-world AI implementation.
Sales Forecasting and Budgeting
Module 1 · 3 Hours to complete
Territory Management
Module 2 · 4 Hours to complete
Sales Performance Evaluation
Module 3 · 2 Hours to complete
Legal and Ethical Issues
Module 4 · 2 Hours to complete
Fee Structure
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Financial Aid
Instructor
Chief Data Scientist and Interim Director at Vanderbilt University
Dr. Jesse Spencer-Smith is the Chief Data Scientist and Interim Director of the Data Science Institute at Vanderbilt University, where he also serves as a Professor of the Practice of Computer Science. He leads a team of data scientists and postdoctoral fellows, collaborating with researchers across the university and industry partners while teaching graduate courses on artificial intelligence. Dr. Spencer-Smith holds a PhD in Cognitive Psychology and Cognitive Science from Indiana University and a BS in Computer Science from the University of Florida. His previous experience includes serving as Director of Data Science at HCA Healthcare, where he established the company's first data science team, and as an Assistant Professor of Psychology in Quantitative Methods at the University of Illinois Urbana-Champaign. He has published extensively on AI applications across various fields and is known for his workshops and speaking engagements on AI for diverse audiences. Dr. Spencer-Smith's work emphasizes the importance of data-driven insights in addressing complex problems across sectors.
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