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SneakPeek- An AI-based Platform for Predicting User Preferences
Category: Social Networking
Services: Gen AI Development, Architecture Design and Review, Managed Engineering Teams
15% increase in generative AI-based personalization
20% reduction in generative AI operational costs.
25% increase in platform’s operational efficiency
About us
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.
Challenge
- 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.
Solution
- 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.
Outcome
- 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.
Architecture Diagram
AWS Services
- Amazon SageMaker: Scalable AI model training and deployment with advanced natural language processing capabilities.
- Claude Sonnet: Generate embeddings in Amazon SageMaker, improving AI capabilities.
- Amazon EC2 C5 Instances: Optimized for compute-intensive tasks, supporting fast processing of AI algorithms.
- Amazon EC2 M5 Instances: General-purpose instances for a balance of compute, memory, and networking.
- Lambda Function: Serverless compute service for running code in response to events, ensuring scalability without infrastructure management.
- Amazon Elemental MediaLive: Live video processing and distribution for real-time streaming.
- Amazon Transcoder: Video transcoding for various device compatibility.
- Amazon Rekognition Image/Video Processing: AI-driven image and video analysis capabilities.
- Amazon RDS PostgreSQL: Secure, scalable data storage for training data with high performance.
- Aurora Amazon RDS Instance: Managed relational database service for high availability and scalability.
- AWS Glue: Comprehensive data management and preprocessing for diverse and unbiased datasets.
- Amazon OpenSearch Service: Full-text search and analytics engine to enhance AI-driven insights.
- Amazon ElastiCache: In-memory caching service for accelerated data access and reduced latency.
- AWS WAF: Web application firewall for protection against web threats.
- AWS Shield: Managed DDoS protection service to safeguard against attacks.
- AWS Certificate Manager: Simplified management of SSL/TLS certificates for secure communications.
- Amazon Cognito User Management: Secure user sign-up, sign-in, and access control.
- Amazon Pinpoint for 2FA over SMS/Call: Two-factor authentication for enhanced security.
- Amazon CloudFront: Content delivery network for fast and secure content distribution.
- Amazon Route 53: Scalable DNS web service for domain management.
- Amazon CloudWatch: Monitoring key performance metrics and setting alerts for any data quality issues.
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