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AI Model Development Lifecycle

1. Exploratory Data Analysis

This is the foundational phase where raw data is collected, cleaned, and transformed to extract meaningful features.

• Data Attributes Identification and Collection: Identifying relevant data attributes from various sources.
• Data Pre-Processing: Cleaning and normalizing data to ensure consistency and quality.
• Feature Engineering: Creating new input features from existing data to improve model performance.

2. Build AI Model

Once the data is prepared, the model building process begins. This includes choosing the appropriate machine learning algorithm and training the model.

• Split Training and Testing Data: Separating data to validate the model’s performance.
• Choose Appropriate ML Algorithm: Selecting the best-suited ML algorithm for the problem.
• Build ML Model with Training Data: Training the selected model using historical data.

3. Tune Model Parameters

Model tuning helps to enhance performance and accuracy by evaluating results and adjusting parameters.

• Evaluate Model with Testing Data: Testing model performance on unseen data.
• Compute Performance Metrics: Calculating metrics such as accuracy, precision, recall, etc.
• Perform Model Tuning: Adjusting hyperparameters and retraining models for better results.

4. Choose and Deploy ML Model

The final step is selecting the best-performing model and deploying it to production environments.

• Iterate through steps 3–9: Continuously improve by revisiting earlier steps.
• Choose Model with Optimum Performance: Selecting the most efficient and accurate model.
• Deploy the Model: Making the model live for business use.

Summary: AI Model Development Lifecycle

The AI Model Development Lifecycle is a structured process that encompasses data preparation, model building, performance tuning, and final deployment. This approach ensures optimal performance and reliability of AI models. Below is a detailed walkthrough of the complete lifecycle as visualized in the image.

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