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World Congress 2025 • Aug 20, 2025 • Session details

distil labs – small model training, made simple

Selim Nowicki

Upgrading vanilla small models used to require specialized machine learning scientists and expensive infrastructure. Now, standard engineering teams can train highly accurate, on-premise expert models in just eight hours.

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#1 about 2 min

The case for deploying small foundational models

Small models provide critical advantages for local deployment, data privacy, and latency-sensitive applications.

#2 about 1 min

The challenge of specializing vanilla small models

Specializing out-of-the-box small models requires significant machine learning expertise and extensive labeled data.

#3 about 1 min

Automating expert model creation with minimal data

Automating the training process empowers developers to build highly accurate expert models using simple prompts.

#4 about 2 min

Applying synthetic data generation and knowledge distillation

Generating synthetic data with large language models automates the fine-tuning process through knowledge distillation.

Matching moments

1:24 min

Training small AI models on secure private data

Alexandre Guenoun Alexandre Guenoun +3 · WWC Europe 2026

2:12 min

Distilling cloud AI capabilities into local device models

3:48 min

Achieving high inference performance in smaller model sizes

Sam Witteveen · Coffee With Developers

6:16 min

The case for fine-tuning small models in agentic AI

Björn Buchhold Björn Buchhold · WWC Europe 2026

4:33 min

Reducing model size using knowledge distillation with teacher-student models

Marek Suppa · LIVE

1:10 min

Adopting fine-tuned task oriented AI models

Chris Heilmann +2 · LIVE

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