AWS For AI podkast

EP12 : Forge & Perform AI Model Customization at Scale | AWS for AI Podcast | Amazon Web Services

21.10.2026
0:00
1:06:36
Do tyłu o 15 sekund
Do przodu o 15 sekund

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.


OUTLINE:

00:00:00 : Introduction

00:00:43 : Meet Oliver — Perform AI

00:03:34 : Meet Karan — AWS Forge

00:07:04 : Why bother customizing when frontier models keep winning?

00:11:51 : The only 3 reasons to customize: quality, speed, cost

00:13:58 : Does the math actually work? The ROI reality

00:19:47 : When you should NOT customize

00:22:11 : How a small model beat the giants in production

00:25:36 : Making the model stop hallucinating

00:28:04 : Pre-training to RL: the 4 stages demystified

00:31:39 : You need way less data than you think

00:39:29 : Catastrophic forgetting (and how to avoid it)

00:44:08 : Why evals matter more than your model

00:48:48 : Prompt vs. fine-tune vs. agent — it's not either/or

00:50:34 : How to actually do it on AWS

00:56:00 : From notebook to production

00:56:15 : Quickfire round

01:01:16 : A CTO's playbook for customization

01:04:53 : Closing remarks


To learn more about Perform AI : https://bit.ly/4ywZ6OM


Parcel Perform case study blog : https://aws.amazon.com/blogs/machine-...



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