Fine-tuning on a Budget
Big models, tight budgets? No problem. In this episode of Pop Goes the stack, hosts Lori MacVittie and Joel Moses talk with Dmitry Kit from F5's AI Center of Excellence about LoRA (Low-Rank Adaptation), the not-so-secret weapon for customizing LLMs without melting your GPU or your wallet. From role-specific agents to domain-aware behavior, we break down how LoRA lets you inject intelligence without retraining the entire brain. Whether you're building AI for IT ops, customer support, or anything in between, this is fine-tuning that actually scales. Learn about the benefits, risks, and practical applications of using LoRA to target specific model behavior, reduce latency, and optimize performance, all for under $1,000. Tune in to understand how LoRA can revolutionize your approach to AI and machine learning.
Creators and Guests
Host
Joel Moses
Distinguished Engineer and VP, Strategic Engineer at F5, Joel has over 30 years of industry experience in cybersecurity and networking fields. He holds several US patents related to encryption technique.
Host
Lori MacVittie
Distinguished Engineer and Chief Evangelist at F5, Lori has more than 25 years of industry experience spanning application development, IT architecture, and network and systems' operation. She co-authored the CADD profile for ANSI NCITS 320-1998 and is a prolific author with books spanning security, cloud, and enterprise architecture.
Guest
Dmitry Kit
Dmitry Kit, PhD is a Senior Principal Data Scientist at F5. He has over 15 years of experience in machine learning operations and artificial intelligence in both academia and industry. He brings deep expertise in developing scalable machine learning systems, optimizing data-driven workflows, and applying AI to solve complex business problems. He holds a PhD in Computer Science from The University of Texas at Austin and has previously held roles at Amazon and Hitachi Vantara, where he contributed to advancing AI-driven solutions across enterprise environments.
