Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access.
In October’s Product School, Alluxio’s Lead Solutions Engineer Greg Palmer will present and demo how Alluxio enables you to embrace the cloud migration strategy or multi-cloud architecture for large-scale analytics and AI workloads. Alluxio also helps scale out your platform adoption for analytics and AI across multiple tenants and applications teams.
Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access.
In October’s Product School, Alluxio’s Lead Solutions Engineer Greg Palmer will present and demo how Alluxio enables you to embrace the cloud migration strategy or multi-cloud architecture for large-scale analytics and AI workloads. Alluxio also helps scale out your platform adoption for analytics and AI across multiple tenants and applications teams.
Video:
Presentation slides:
Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access.
In October’s Product School, Alluxio’s Lead Solutions Engineer Greg Palmer will present and demo how Alluxio enables you to embrace the cloud migration strategy or multi-cloud architecture for large-scale analytics and AI workloads. Alluxio also helps scale out your platform adoption for analytics and AI across multiple tenants and applications teams.
Videos:
Presentation Slides:
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Videos
Deepseek’s recent announcement of the Fire-flyer File System (3FS) has sparked excitement across the AI infra community, promising a breakthrough in how machine learning models access and process data.
In this webinar, an expert in distributed systems and AI infrastructure will take you inside Deepseek 3FS, the purpose-built file system for handling large files and high-bandwidth workloads. We’ll break down how 3FS optimizes data access and speeds up AI workloads as well as the design tradeoffs made to maximize throughput for AI workloads.
This webinar you’ll learn about how 3FS works under the hood, including:
✅ The system architecture
✅ Core software components
✅ Read/write flows
✅ Data distribution/placement algorithms
✅ Cluster/node management and disaster recovery
Whether you’re an AI researcher, ML engineer, or infrastructure architect, this deep dive will give you the technical insights you need to determine if 3FS is the right solution for you.