developing ai checklist
Project Planning and Definition
Data Collection and Preparation
Model Selection and Development
Testing and Validation
- Identify key performance indicators (KPIs) relevant to the problem.
- Select metrics such as accuracy, precision, recall, F1 score, etc.
- Establish baseline performance levels for comparison.
- Ensure metrics align with business objectives and user needs.
- Split dataset into training and validation sets.
- Use k-fold cross-validation for reliable performance estimates.
- Identify hyperparameters to tune using grid or random search.
- Evaluate model performance for each combination of hyperparameters.
- Prepare a separate validation dataset not used in training.
- Run the model on the validation dataset to assess performance.
- Compare results against predefined evaluation metrics.
- Analyze any discrepancies and adjust models as necessary.
- Record performance metrics and evaluation outcomes.
- Summarize model behavior and any observed anomalies.
- Provide insights into model strengths and weaknesses.
- Include recommendations for future improvements and iterations.
Use in Manifestly