ING Bank is a multinational financial services company headquartered in Amsterdam with over $1 trillion in assets. As a leading bank, we place a great emphasis on cybersecurity. One aspect of this is the Security incident and event management (SIEM), which is the process of identifying, monitoring, recording and analyzing security events or incidents within a real-time IT environment. SIEM requires our data platform to have high and consistent performance, so we use open source technologies Presto and Alluxio for fast SQL analytics in the cloud.
In this online presentation, we are going to present how ING is leveraging Presto (interactive query), Alluxio (data orchestration & acceleration), S3 (massive storage), and DC/OS (container orchestration) to build and operate our modern Security Analytics & Machine Learning platform. We will share the challenges we encountered and how we solved them. Today we run this platform in several different data centers, and we have reduced our 10+ minutes queries to under 10 seconds!
ING Bank is a multinational financial services company headquartered in Amsterdam with over $1 trillion in assets. As a leading bank, we place a great emphasis on cybersecurity. One aspect of this is the Security incident and event management (SIEM), which is the process of identifying, monitoring, recording and analyzing security events or incidents within a real-time IT environment. SIEM requires our data platform to have high and consistent performance, so we use open source technologies Presto and Alluxio for fast SQL analytics in the cloud.
In this online presentation, we are going to present how ING is leveraging Presto (interactive query), Alluxio (data orchestration & acceleration), S3 (massive storage), and DC/OS (container orchestration) to build and operate our modern Security Analytics & Machine Learning platform. We will share the challenges we encountered and how we solved them. Today we run this platform in several different data centers, and we have reduced our 10+ minutes queries to under 10 seconds!
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Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
