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Időtartalma 7 órák
Kurzusleírás
From Text Generation to Typed Decisions
- What a decision model returns
- System one decisions versus reasoning LLMs
- Cost and latency profile of bounded decisions
The Open-Weight Decision Model Landscape
- Strands Decider 2B from AWS Labs
- JEV and GEV open-weight families
- Pplx-Decider, Liquid D1 and Clef
- Licence and deployment differences
Inside a Decision Model
- LLM torso without a language modelling head
- Pointer head scoring over candidate options
- Where the confidence score comes from
Running Your First Decisions
- Local setup on CPU and consumer GPU
- Phrasing questions and option sets
- Asking several questions in one pass
- Thresholds and fallback behaviour
Judging Decision Quality
- JevBench and decision benchmarks
- Accuracy versus calibration
- Brier score and reliability of confidence
- Tasks where a decision model is the wrong tool
Követelmények
- Basic familiarity with large language models and prompt-based applications.
- Comfort with the command line and Python scripts.
- No machine learning training experience required.
Audience
- AI engineers evaluating decision models for agent workflows
- Software developers replacing LLM calls with bounded decisions
- Solution and platform architects designing agent architectures
- Technical product managers assessing decision model options
