Large Language Models: Strategies and Best Practices with Sinan Ozdemir ODSC's Ai X Podcast
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- Technology
In this episode of ODSC’s Ai X Podcast, hear from Sinan Ozdemir, author of the Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs. This wide-ranging discussion will take you through Sinan’s inspiration and motivations for writing this book on LLMs, as well as common questions and challenges that arise when working with LLMs. Finally, Sinan shares his take on what the future of LLMs might look like.
Be the first to know when new episodes drop – subscribe now.
Sponsored by: https://odsc.com/
Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here – https://aiplus.training/
Topics:
Background and Motivation:
1. What inspired you to write this book on Large Language Models?
2. Understanding LLMs: Can you explain how Large Language Models work and why they have become so significant in recent years?
3. Is your book aimed at newcomers in the field of AI and LLMs? And does it include insights that experienced practitioners would find useful?
Applications and Use Cases:
1. Your book covers various applications of LLMs. Which application do you find most fascinating or promising?
Getting the Most Out of LLMs:
1. Search is a popular use case you go into detail in your book. Tell us how semantic search can be improved with LLMs
2. When choosing an LLM for a specific task, what factors should be considered beyond just model parameter size and model performance?
Advanced LLM Usage:
1. The book delves into multimodal models in Chapter 7. Multimodal models generate a lot of excitement. Can you give us some insights on how multimodal models work and is the excitement justified?
Technical Deep Dives:
1. Could you discuss the importance of prompt engineering and fine-tuning in optimizing LLMs' performance?
2. Chapter 8 focuses on open-source LLM fine-tuning. What are the main advantages and disadvantages compared to using closed-source models?
3. What are some more advanced Open-Source LLM Fine-Tuning techniques?
Moving LLMs into Production:
1. What are the biggest technical and logistical hurdles organizations need to overcome when deploying LLMs in production environments?
Deployment and Production:
1. What are the main challenges in moving LLMs from the pilot stage to production, and does your book have any advice for this process?
2. Can you share insights on cost management and efficiency when deploying LLMs in a production environment?
Future Outlook:
1. Looking ahead, what do you anticipate as the most impactful applications of LLMs in the next year or so?
2. How should individuals and organizations prepare for the advancements in LLMs such as GPT-5?
3. Throughout your journey in writing this book, were there any surprises you encountered about LLMs and GenAi?
4. Where is the best place to purchase your book and how do people follow your work?
Some useful links:
Special Offer from InformIT: Save 40% off the list price of the print or eBook edition of Quick Start Guide to LLMs when you use discount code ODSCAI at checkout.
https://www.informit.com/store/quick-start-guide-to-large-language-models-strategies-9780138199197
In this episode of ODSC’s Ai X Podcast, hear from Sinan Ozdemir, author of the Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs. This wide-ranging discussion will take you through Sinan’s inspiration and motivations for writing this book on LLMs, as well as common questions and challenges that arise when working with LLMs. Finally, Sinan shares his take on what the future of LLMs might look like.
Be the first to know when new episodes drop – subscribe now.
Sponsored by: https://odsc.com/
Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here – https://aiplus.training/
Topics:
Background and Motivation:
1. What inspired you to write this book on Large Language Models?
2. Understanding LLMs: Can you explain how Large Language Models work and why they have become so significant in recent years?
3. Is your book aimed at newcomers in the field of AI and LLMs? And does it include insights that experienced practitioners would find useful?
Applications and Use Cases:
1. Your book covers various applications of LLMs. Which application do you find most fascinating or promising?
Getting the Most Out of LLMs:
1. Search is a popular use case you go into detail in your book. Tell us how semantic search can be improved with LLMs
2. When choosing an LLM for a specific task, what factors should be considered beyond just model parameter size and model performance?
Advanced LLM Usage:
1. The book delves into multimodal models in Chapter 7. Multimodal models generate a lot of excitement. Can you give us some insights on how multimodal models work and is the excitement justified?
Technical Deep Dives:
1. Could you discuss the importance of prompt engineering and fine-tuning in optimizing LLMs' performance?
2. Chapter 8 focuses on open-source LLM fine-tuning. What are the main advantages and disadvantages compared to using closed-source models?
3. What are some more advanced Open-Source LLM Fine-Tuning techniques?
Moving LLMs into Production:
1. What are the biggest technical and logistical hurdles organizations need to overcome when deploying LLMs in production environments?
Deployment and Production:
1. What are the main challenges in moving LLMs from the pilot stage to production, and does your book have any advice for this process?
2. Can you share insights on cost management and efficiency when deploying LLMs in a production environment?
Future Outlook:
1. Looking ahead, what do you anticipate as the most impactful applications of LLMs in the next year or so?
2. How should individuals and organizations prepare for the advancements in LLMs such as GPT-5?
3. Throughout your journey in writing this book, were there any surprises you encountered about LLMs and GenAi?
4. Where is the best place to purchase your book and how do people follow your work?
Some useful links:
Special Offer from InformIT: Save 40% off the list price of the print or eBook edition of Quick Start Guide to LLMs when you use discount code ODSCAI at checkout.
https://www.informit.com/store/quick-start-guide-to-large-language-models-strategies-9780138199197