# ADVSUS10-BP01 Optimize content moderation systems for sustainable operation
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 As content grows for organizations, optimizing content moderation systems can benefit sustainability-related key performance indicators (KPIs). Implement or build architectures that include efficient machine learning models, automated scaling, and optimized storage patterns. 

## Implementation guidance
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+  Use efficient machine learning models for content classification. Use AWS Inferentia chips when possible, for improved performance per watt. 
+  Implement batch processing for non-real-time moderation tasks. 
+  Configure regional content analysis to minimize data movement. 
+  Use caching strategies for frequently accessed moderation rules. 
+  Use energy-efficient computing resources, such as AWS Graviton, for moderation workloads. 
+  Implement automated scaling based on moderation demand using auto scaling rules and Amazon CloudWatch metrics. 
+  Optimize storage patterns for moderation results and audit trails. For workloads using Amazon S3, use Storage Lens for insights and recommendations to optimize storage use. 

## Key AWS services
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+  Amazon Rekognition 
+  AWS Inferentia 
+  Amazon SageMaker AI 
+  AWS Auto Scaling 
+  AWS CloudWatch 
+  Amazon ElastiCache 
+  Amazon S3 Storage Lens 

## **Resources**
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+  [Optimize AI/ML workloads for sustainability: Part 1, identify business goals, validate ML use, and process data](https://aws.amazon.com/blogs/architecture/optimize-ai-ml-workloads-for-sustainability-part-1-identify-business-goals-validate-ml-use-and-process-data/)