Join host Hamza Mimi for a deep conversation on model customization with two guests tackling it from opposite vantage points. Oliver Gracer, Head of Data & Insights at Perform.AI (formerly Parcel Perform) — the Singapore-based delivery experience platform processing billions of shipments across 160+ countries — shares how his team fine-tuned Amazon Nova and achieved something counter-intuitive: a smaller model that outperformed larger ones on accuracy, cost, and speed. Joining him is Karan Pandharkar, Principal Product Manager for Forge and model customization at AWS, who reveals the patterns, anti-patterns, and tools he sees across customers worldwide.Together they unpack why customization is an economics question before it's a technology question, and give a practical roadmap for teams moving from prompting to production customization.
This episode covers critical topics including:
• Why and when to customize vs. prompt engineer, RAG, or build an agentic harness
• Building a robust evaluation framework and defining what "good" looks like
• The ROI conversation: fine-tuning cost vs. inference cost (and talking to your CFO)
• The hallucination problem and how fine-tuning tackled it
• Pre-training, mid-training, fine-tuning, and reinforcement learning explained
• Data quality over quantity — and how little data you may actually need
• Catastrophic forgetting and the role of general-purpose data mixing
• LoRA / parameter-efficient fine-tuning and the pay-as-you-go economics of serving
• Customizing with SageMaker AI, Amazon Bedrock, and Nova Forge
Whether you're a startup, enterprise, or public sector organization running high-volume AI workloads, this episode offers invaluable guidance on building scalable, cost-efficient, production-ready AI solutions in today's rapidly evolving landscape.
To learn more about Perform.AI : https://bit.ly/4ywZ6OM
Parcel Perform case study blog : https://aws.amazon.com/blogs/machine-learning/fine-tune-amazon-nova-models-for-accurat
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