Sinan Ozdemir outlines how to decide whether model fine-tuning is justified by measurable quality, latency and cost needs, compared with improvements elsewhere in an AI system.
Sinan Ozdemir
Head of AI Developer Education · Fireworks AI
7Sep
5:00 PM UTC · 60 min
What you’ll explore
1
Compare available interventions
Weigh prompting, retrieval, serving changes and model training.
2
Assess the full tradeoff
Consider data preparation, maintenance, quality, cost and deployment complexity.
3
Use evidence to decide
Structure technical discussions around evaluation results and failure types.
About this session
Sinan Ozdemir outlines how to decide whether model fine-tuning is justified by measurable quality, latency and cost needs, compared with improvements elsewhere in an AI system.
Guest speaker
Sinan Ozdemir
Head of AI Developer Education · Fireworks AI
Sinan creates developer education around inference and training. He is an AI author and the founder of Kylie.ai.
Hosted by Aishwarya Srinivasan and Arvind Narayanamurthy.
Aishwarya Srinivasan
AI Advisor / AI Educator
Aishwarya Srinivasan is the Co-Founder of The Gen Academy and a Developer Relations Lead at Nebius. Her experience spans Fireworks AI, Microsoft for Startups, Google Cloud and IBM, where she worked with developers and businesses on practical AI and machine-learning solutions. She holds a postgraduate degree in Data Science from Columbia University and teaches AI engineering, applied machine learning, generative AI and LLMOps.
Founder @ The Gen Academy | AI Solutions Architect | Ex - Adobe, Microsoft, IBM
Arvind brings a cross-company perspective on AI at scale, built across Microsoft, IBM, and Adobe, where he led data science teams and developed production-grade machine-learning solutions. He has built and deployed enterprise AI systems and focuses on translating AI concepts into practical, production-ready solutions. He holds a Master's from Carnegie Mellon University.
