Answer: Use Amazon SageMaker and iterate with newer data.

## Explanation **Amazon SageMaker** is the correct choice because: 1. **ML Model Development**: Amazon SageMaker is AWS's fully managed machine learning service designed specifically for building, training, and deploying ML models. 2. **Continuous Improvement**: The requirement to "continuously improve the model's accuracy" aligns perfectly with SageMaker's capabilities for iterative model refinement using newer data. 3. **Iterative Process**: SageMaker supports the entire ML lifecycle, including data preprocessing, model training, hyperparameter tuning, and deployment, allowing for continuous iteration with fresh data. **Why other options are incorrect:** - **B. Amazon Personalize**: This is a recommendation service, not a general-purpose ML development platform. While it uses ML, it's specialized for recommendations rather than custom model development for food waste reduction. - **C. Amazon CloudWatch**: This is a monitoring and observability service, not an ML development platform. It can monitor applications but cannot develop or improve ML models. - **D. Amazon Rekognition**: This is a computer vision service for image and video analysis, not a general ML development platform for custom models related to food waste prediction. **Key AWS Concepts:** - **Amazon SageMaker**: End-to-end ML service for building, training, and deploying models - **ML Lifecycle**: Data collection → Model training → Evaluation → Deployment → Monitoring → Retraining - **Continuous Improvement**: The ability to retrain models with new data to improve accuracy over time

Author: Ritesh Yadav