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AI Engineer Checklist

1. Project Planning

Define project goals and objectives.

Identify stakeholders and their requirements.

Conduct a feasibility study for AI implementation.

Determine the budget and resources needed.

2. Data Collection and Preparation

Identify data sources relevant to the project.

Collect and aggregate data from various sources.

Clean and preprocess data for analysis (handling missing values, outliers, etc.).

Perform exploratory data analysis (EDA) to understand data distributions and patterns.

3. Model Selection

Review existing AI models relevant to the problem domain.

Evaluate the trade-offs between different algorithms (e.g., accuracy, interpretability, training time).

Choose appropriate evaluation metrics for model performance.

4. Model Development

Set up the development environment (libraries, frameworks, tools).

Implement the chosen algorithms and models.

Tune hyperparameters to optimize model performance.

Document code and methodologies used during development.

5. Model Training and Validation

Split data into training, validation, and test sets.

Train the model using the training set.

Validate the model using the validation set and adjust as necessary.

Evaluate the final model performance on the test set.

6. Deployment

Prepare the model for deployment (containerization, API development).

Choose a deployment strategy (cloud, edge, on-premises).

Implement monitoring tools to track model performance post-deployment.

Ensure compliance with data privacy and security regulations.

7. Maintenance and Iteration

Monitor model performance over time and retrain as necessary.

Gather user feedback and adapt the model to changing requirements.

Stay updated with the latest advancements in AI and integrate new techniques as needed.

8. Documentation and Reporting

Create comprehensive documentation for the model, including architecture, data sources, and usage guidelines.

Prepare reports for stakeholders outlining project progress, challenges, and outcomes.

Ensure knowledge transfer within the team through presentations or workshops.

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