AWS Certified Cloud Practitioner

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