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|Intelligence & communication
The need clears up and the right scope becomes visible.
When model choice, data preparation and success measurement are not considered together, an AI project does not pay off in production. Application context is what makes a model meaningful.
대상
- Teams that need prediction, classification, search, recommendation or text processing.
DELIVERABLES
- Problem and data assessment
- Model usage scenario
- Prototype or first working module
- Evaluation criteria
- Human control and monitoring plan
TECHNOLOGY USED
Python, data processing, model APIs, evaluation sets, search layer, observability and secure application services.
예상 범위
The first version is limited to one model or one workflow; data quality and expected accuracy define the scope.
프로세스
- 01We define the data and the decision.
- 02We set the success measure and the cost of error.
- 03We run a small experiment.
- 04We add the human and record layer to the production flow.
- 05We measure and improve the results.