SneakPeek- An AI-based Platform for Predicting User Preferences

SneakPeek is a location-based social networking platform that allows users to interact with their local community, discover new places, and follow others in their area. It offers customized content, location-specific tags, and filters. Brands can leverage SneakPeek to connect with their local audiences. SneakPeek wanted to build a generative AI platform that generated personalized user content based on preferences, location history, and engagement patterns.

• Scaling generative AI models to handle increasing data and user requests without performance degradation.
• Developing AI algorithms to provide accurate, bias-free recommendations, ensuring fairness across various user demographics.
• Ensuring that AI-generated content aligns with real-world experiences and maintains user engagement and trust.
• Implementing robust data protection measures and transparent policies to comply with privacy regulations and retain user trust.
• Obtaining high-quality, unbiased, location-based training data at scale for generative AI modules.

• Scaling generative AI models for personalized content: We utilized scalable generative AI infrastructure to handle the increasing volume of user data and personalization demands, ensuring seamless performance across the platform.
• Enhancing content personalization with Claude Sonnet: Claude Sonnet was integrated within Amazon SageMaker to generate highly accurate embeddings, improving the relevance of personalized content by mapping user preferences and behaviors effectively.
• Implementing MLOps for efficient AI deployment: Continuous integration and delivery of AI models were established using MLOps best practices to automate testing, validation, and deployment of generative AI models, improving operational efficiency and accuracy.
• Optimizing data pipelines for bias-free AI recommendations: High-quality data preprocessing pipelines were developed to ensure that AI models remain unbiased, leveraging comprehensive data management tools for training generative AI algorithms.
• Ensuring real-time personalization with auto-scaling: We implemented auto-scaling mechanisms to dynamically adjust the platform’s resources based on user activity, ensuring real-time personalization without performance drops during high traffic periods.
• Monitoring model performance for responsible AI: We continuously monitored the performance of AI models, focusing on detecting data drift and ensuring the accuracy of generative content to maintain user trust and engagement.

• 15% increase in generative AI platform personalization: Efficient scaling through Amazon SageMaker and custom embeddings enhanced user-specific content generation.
• 20% reduction in generative AI operational costs: Optimized resource usage and cost monitoring through AWS Cost Explorer reduced expenses.
• 25% improvement in platform efficiency: Automation of build, test, and deployment cycles with AWS CodeBuild and MLOps pipeline integration led to a more agile operational environment.