On-Demand Videos
Coupang is a leading e-commerce company in South Korea, with over 50,000 employees and $20+ billion in annual revenue. Coupang's AI platform team builds and manages a large-scale AI platform in AWS for machine learning engineers to train models that enhance and customize product search results and product recommendations for its 100+ million customers.
As the search and recommendation models evolve, optimizing the underlying infrastructure for AI/ML workloads is essential for the e-commerce business. Coupang's platform team actively sought to improve their model training pipeline to boost machine learning engineers' productivity, publish models to production faster, and reduce operational costs.
Coupang focused on addressing several key areas:
- Shortening data preparation and model training time
- Improving GPU utilization in training clusters in different regions
- Reducing S3 API and egress costs incurred from copying large training datasets across regions
- Simplifying the operational complexity of storage system management
In this tech talk, Hyun Jung Baek, Staff Backend Engineer at Coupang, will share best practices for leveraging distributed caching to power search and recommendation model training infrastructure.
Hyun will discuss:
- How Coupang builds a world-class large-scale AI platform for machine learning engineers to deliver better search and recommendation models
- How adding distributed caching to their multi-region AI infrastructure improves GPU utilization, accelerates end-to-end training time, and significantly reduces cross-region data transfer costs.
- How to simplify platform operations and to easily deploy the same architecture to new GPU clusters.
About the Speaker
Hyun Jung Baek is a Staff Backend Engineer at Coupang.
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.
.png)
Running AI/ML workloads in different clouds present unique challenges. The key to a manageable multi-cloud architecture is the ability to seamlessly access data across environments with high performance and low cost.
This webinar is designed for data platform engineers, data infra engineers, data engineers, and ML engineers who work with multiple data sources in hybrid or multi-cloud environments. Chanchan and Bin will guide the audience through using Alluxio to greatly simplify data access and make model training and serving more efficient in these environments.
You will learn:
- How to access data in multi-region, hybrid, and multi-cloud like accessing a local file system
- How to run PyTorch to read datasets and write checkpoints to remote storage with Alluxio as the distributed data access layer
- Real-world examples and insights from tech giants like Uber, AliPay and more
Cloud-native model training jobs require fast data access to achieve shorter training cycles. Accessing data can be challenging when your datasets are distributed across different regions and clouds. Additionally, as GPUs remain scarce and expensive resources, it becomes more common to set up remote training clusters from where data resides. This multi-region/cloud scenario introduces the challenges of losing data locality, resulting in operational overhead, latency and expensive cloud costs.
In the third webinar of the multi-cloud webinar series, Chanchan and Shawn dive deep into:
- The data locality challenges in the multi-region/cloud ML pipeline
- Using a cloud-native distributed caching system to overcome these challenges
- The architecture and integration of PyTorch/Ray+Alluxio+S3 using POSIX or RESTful APIs
- Live demo with ResNet and BERT benchmark results showing performance gains and cost savings analysis
In this presentation, Bin Fan (VP of Open Source @ Alluxio) will address a critical challenge of optimizing data loading for distributed Python applications within AI/ML workloads in the cloud, focusing on popular frameworks like Ray and Hugging Face. Integration of Alluxio’s distributed caching for Python applications is accomplished using the fsspec interface, thus greatly improving data access speeds. This is particularly useful in machine learning workflows, where repeated data reloading across slow, unstable or congested networks can severely affect GPU efficiency and escalate operational costs.
Attendees can look forward to practical, hands-on demonstrations showcasing the tangible benefits of Alluxio’s caching mechanism across various real-world scenarios. These demos will highlight the enhancements in data efficiency and overall performance of data-intensive Python applications. This presentation is tailored for developers and data scientists eager to optimize their AI/ML workloads. Discover strategies to accelerate your data processing tasks, making them not only faster but also more cost-efficient.
As GenAI and AI continue to transform businesses, scaling these workloads requires optimized underlying infrastructure. A multi-cloud architecture allows organizations to leverage different cloud services to meet diverse workload demands while maximizing efficiency, reducing costs, and avoiding vendor lock-in. However, achieving a multi-cloud vision can be challenging.
In this webinar, Tarik will share how an agonistic data layer, like Alluxio, allows you to embrace the separation of storage from compute and simplify the adoption of multi-cloud for AI.
- Learn why leveraging multiple cloud providers is critical for balancing performance, scalability, and cost of your AI platform
- Discover how an agnostic data layer like Alluxio provides seamless data access in multi-cloud that bridges storage and compute without data replication
- Gain insights into real-world examples and best practices for deploying AI across on-prem, hybrid, and multi-cloud environments
2024 is gearing up to be an impactful year for AI and analytics. Join us on January 30, as Kevin Petrie (VP of Research at Eckerson Group) and Omid Razavi (SVP of Customer Success at Alluxio) share key trends that data and AI leaders should know. This event will efficiently guide you with market data and expert insights to drive successful business outcomes.
- Assess current and future trends in data and AI with industry experts
- Discover valuable insights and practical recommendations
- Learn best practices to make your enterprise data more accessible for both analytics and AI applications
Uber builds one of the biggest data lakes in the industry, which stores exabytes of data. In this talk, we will introduce the evolution of our data storage architecture, and delve into multiple key initiatives during the past several years.
Specifically, we will introduce:
- Our on-prem HDFS cluster scalability challenges and how we solved them
- Our efficiency optimizations that significantly reduced the storage overhead and unit cost without compromising reliability and performance
- The challenges we are facing during the ongoing Cloud migration and our solutions
Shengxuan Liu from ByteDance presents the new ByteDance’s native Parquet Reader. The talk covers the architecture and key features of the Reader, and how the new Reader is able to facilitate data processing efficiency.
In this session, cloud optimization specialists Chunxu and Siyuan break down the challenges and present a fresh architecture designed to optimize I/O across the data pipeline, ensuring GPUs function at peak performance. The integrated solution of PyTorch/Ray + Alluxio + S3 offers a promising way forward, and the speakers delve deep into its practical applications. Attendees will not only gain theoretical insights but will also be treated to hands-on instructions and demonstrations of deploying this cutting-edge architecture in Kubernetes, specifically tailored for Tensorflow/PyTorch/Ray workloads in the public cloud.
In this session, Jingwen presents an overview of using Alluxio Edge caching to accelerate Trino or Presto queries. She offers practical best practices for using distributed caching with compute engines. In addition, this session also features insights from real-world examples.
As a cache eviction algorithm, FIFO has a lot of attractive properties, such as simplicity, speed, scalability, and flash-friendliness. The most prominent criticism of FIFO is its low efficiency (high miss ratio). In this talk, Juncheng Yangb describes a simple, scalable FIFO-based algorithm with three static queues (S3-FIFO). Evaluated on 6594 cache traces from 14 datasets, we show that S3- FIFO has lower miss ratios than state-of-the-art algorithms across traces. Moreover, S3-FIFO’s efficiency is robust — it has the lowest mean miss ratio on 10 of the 14 datasets. FIFO queues enable S3-FIFO to achieve good scalability with 6× higher throughput compared to optimized LRU at 16 threads. Our insight is that most objects in skewed workloads will only be accessed once in a short window, so it is critical to evict them early (also called quick demotion). The key of S3-FIFO is a small FIFO queue that filters out most objects from entering the main cache, which provides a guaranteed demotion speed and high demotion precision.
Many companies are working with development architectures for AI platforms but have concerns about efficiency at scale as data volumes increase. They use centralized cloud data lakes, like S3, to store training data for AI platforms. However, GPU shortages add more complications. Storage and compute can be separate, or even remote, making data loading slow and expensive:
- Optimizing a developmental setup can include manual copies, which are slow and error-prone
- Directly transferring data across regions or from cloud to on-premises can incur expensive egress fees
This webinar covers solutions to improve data loading for model training. You will learn:
- The data loading challenges with distributed infrastructure
- Typical solutions, including NFS/NAS on object storage, and why they are not the best options
- Common architectures that can improve data loading and cost efficiency
- Using Alluxio to accelerate model training and reduce costs