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Enhancing User Engagement with AWS Cloud
Instagram needed to handle billions of photo and video uploads daily while delivering a seamless user experience across the globe.

Solution
Migrated storage infrastructure to Amazon S3 for scalable storage and fast content retrieval.
Used Amazon DynamoDB to store metadata for posts, comments, and user interactions.
Implemented Amazon SageMaker to develop AI models for content moderation and personalized feed recommendations.
Result
- Reduced image and video load time by 30%.
- Enhanced content moderation accuracy by 25%.
- Achieved 99.99% uptime during peak usage periods.
Improving Video Streaming and AI Recommendations with Google Cloud
TikTok required a robust and scalable cloud infrastructure to support its rapid growth, process billions of user-generated videos, and deliver personalized content recommendations.

Solution
Migrated video processing workloads to Google Cloud Platform (GCP) for high-speed processing.
Used Google BigQuery to analyze user behavior and preferences in real time.
Leveraged Vertex AI to refine its content recommendation engine.
Result
- Improved video recommendation accuracy by 28%.
- Reduced video processing latency by 20%.
- Scaled seamlessly to support over 1 billion active users.
Strengthening Security and Analytics with Azure Cloud
Twitter aimed to enhance its platform security, improve data analytics, and support its growing user base with a secure, scalable cloud infrastructure.

Solution
Migrated analytical workloads to Microsoft Azure to enhance performance.
Used Azure Sentinel for real-time threat detection and mitigation.
Leveraged Azure Synapse Analytics to process and analyze vast amounts of user data for personalized ad delivery.
Result
- Reduced security threats by 35% through real-time monitoring.
- Improved ad targeting accuracy by 20%.
- Achieved 99.9% system uptime during high-traffic events.
Optimizing Augmented Reality (AR) Features with AWS Cloud
Snapchat wanted to scale its Augmented Reality (AR) features and improve real-time processing for its interactive filters and lenses.

Solution
Migrated AR workloads to AWS Cloud for faster processing.
Used Amazon EC2 for running AR rendering models efficiently.
Implemented Amazon CloudFront for low-latency content delivery worldwide.
Result
- Reduced AR rendering time by 22%.
- Increased user engagement with faster and more responsive AR filters.
- Scaled infrastructure to handle millions of daily AR interactions.