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Distilled Engineering
Eight field guides adapted from Distilled: The Engineering of Small, Fast, Cheap AI Models, from transfer signals and synthetic curricula to release pipelines and specialist fleets.
active1 / 8 episodes•13 min total•advanced
Series Progress13%
What You'll Learn
- ✓Decide whether a distilled student fits the workload and economics
- ✓Choose transfer signals without hiding access or compatibility limits
- ✓Build synthetic curricula with provenance, validation, and quarantine
- ✓Select compression experiments from the binding production resource
- ✓Turn generation, training, evaluation, and release into a resumable artifact graph
- ✓Route specialist models with policy, capacity, calibration, and fallback gates
Episodes
⚗️All Episodes (1/8)
7 more episodes coming soon
Prerequisites
- •Transformer and supervised fine-tuning fundamentals
- •Basic model evaluation
- •Production machine-learning systems
Who This Is For
- •ml-engineers
- •ai-engineers
- •platform-engineers
- •technical-leaders