Category: Case Studies
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Using Amazon Neptune for Real-time Anomaly Detection in Gaming Transactions
Discover how Zupee leveraged Amazon Neptune’s graph database to detect real-time anomalies in gaming wallet transactions. Learn how they overcame relational database limitations to build an integrity system that processes over 1 million daily transactions, identifies suspicious patterns, and ensures incentives reach legitimate users.
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How Flutter UKI Optimized Data Pipelines with Amazon MWAA
Discover how Flutter UKI transformed their data pipelines by migrating from EC2-based Airflow to Amazon MWAA, managing 5,500 DAGs and 60,000 daily runs with improved stability and reduced operational overhead.
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MIT’s SASA Method: Training LLMs to Self-Detoxify Their Language Output
MIT researchers have developed SASA, a method allowing Large Language Models to detoxify their own outputs without retraining. This system creates internal boundaries between toxic/non-toxic subspaces, helping LLMs generate appropriate content while maintaining natural language fluency—similar to how humans develop internal filters for appropriate speech.
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Instagram’s Journey to Managing 1000+ ML Models
Instagram has successfully scaled its recommendation system to manage over 1000 ML models. This article explores how they built a robust infrastructure through a model registry, streamlined launching process, and innovative stability metrics to maintain high-quality personalized experiences for billions of users.
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Building an Operational Data Store with AWS Purpose-Built Databases for Finance Applications
Discover how Amazon Finance Automation built a high-performance operational data store using AWS purpose-built databases. Learn how they combined DynamoDB, OpenSearch, and Neptune to handle hundreds of millions of daily financial transactions with millisecond latency while maintaining flexibility and data accuracy.
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Enhancing Netflix Recommendations with FM-Intent: Predicting User Session Intent
Netflix has developed FM-Intent, an advanced recommendation model that predicts user intent during viewing sessions. By understanding whether users want to discover new content, continue watching shows, or explore specific genres, FM-Intent delivers 7.4% more accurate recommendations than previous systems, creating a more personalized streaming experience.
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The Path to AGI: Balancing Innovation with Responsibility and Safety
Google DeepMind is pioneering a responsible approach to Artificial General Intelligence (AGI), balancing innovation with safety. Their framework addresses misuse, misalignment, and transparency while collaborating with global partners to ensure this transformative technology benefits humanity safely.
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Reducing Carbon Footprint with Google’s Hardware Harvesting Program
Google’s hardware harvesting program is reducing carbon emissions by reusing existing hardware components in data centers instead of buying new ones. In 2024, they repurposed over 293,000 components, saving costs and reducing environmental impact while supporting their net-zero goals.

