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Questions and Answers
What is the first step in the AI project cycle?
What is the first step in the AI project cycle?
- Model training and evaluation
- Feature engineering
- Deployment and monitoring
- Data collection and preprocessing (correct)
Which phase involves transforming raw data into a format suitable for modeling?
Which phase involves transforming raw data into a format suitable for modeling?
- Model training and evaluation
- Model deployment and monitoring
- Feature engineering (correct)
- Data collection and preprocessing
When does the continuous improvement and optimization of the AI model occur in the project cycle?
When does the continuous improvement and optimization of the AI model occur in the project cycle?
- At the end of the project cycle
- During model deployment and monitoring (correct)
- Before data collection and preprocessing
- After feature engineering
What is the process of transforming raw data into a format suitable for modeling called?
What is the process of transforming raw data into a format suitable for modeling called?
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When does the continuous improvement and optimization of the AI model typically occur in the project cycle?
When does the continuous improvement and optimization of the AI model typically occur in the project cycle?
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What is the first step in the AI project cycle?
What is the first step in the AI project cycle?
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Study Notes
AI Project Cycle Overview
- The first step in the AI project cycle is defining the problem or goal to be achieved.
- This initial phase sets the direction for the entire project by clarifying what needs to be solved.
Data Preparation Phase
- The process of transforming raw data into a format suitable for modeling is known as data preprocessing.
- Data preprocessing involves cleaning, normalizing, and aggregating data to ensure it is high quality for analysis.
Continuous Improvement and Optimization
- Continuous improvement and optimization of the AI model typically occur in the deployment and maintenance phase.
- This stage includes refining the model based on user feedback, performance metrics, and changing data patterns, ensuring sustained efficacy.
Recap of Key Terms
- Data Preprocessing: The critical step to prepare raw data for effective modeling.
- Continuous Improvement: An ongoing process in the project cycle aimed at enhancing model accuracy and performance post-deployment.
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