As AI models grow larger and more computationally intensive, deploying them efficiently in real-world environments becomes a critical challenge. This session explores three of the most effective model optimization techniques used in production AI systems: pruning, quantization, and knowledge distillation.
Attendees will discover how to dramatically reduce memory footprint, accelerate inference latency, and minimize hardware compute costs without sacrificing model accuracy or task performance. We will evaluate trade-offs, discuss practical deployment scenarios across cloud and edge infrastructure, and share battle-tested strategies for delivering lightweight, production-ready AI models.
Key Takeaways
- Pruning Techniques: Learn how to remove redundant weights and architectural structures to compress models effectively.
- Quantization Strategies: Understand post-training quantization (PTQ) and quantization-aware training (QAT) to optimize precision and compute throughput.
- Knowledge Distillation: Master transferring knowledge from large, compute-heavy teacher models to compact, lightning-fast student models.
- Production Trade-offs: Balance inference latency, memory constraints, and accuracy across enterprise cloud and edge environments.
Target Audience
- Tech engineers, software developers, AI practitioners, and engineering leaders seeking to optimize and scale AI deployments efficiently.
Prerequisites
- None. Basic familiarity with machine learning models and inference workflows is helpful.