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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.