On-Demand Videos
Scaling experimentation in digital marketplaces is crucial for driving growth and enhancing user experiences. However, varied methodologies and a lack of experiment governance can hinder the impact of experimentation leading to inconsistent decision-making, inefficiencies, and missed opportunities for innovation.
At Poshmark, we developed a homegrown experimentation platform, Lightspeed, that allowed us to make reliable and confident reads on product changes, which led to a 10x growth in experiment velocity and positive business outcomes along the way.
This session will provide a deep dive into the best practices and lessons learned from successful implementations of large-scale experiments. We will explore the importance of experimentation, overcome scalability challenges, and gain insights into the frameworks and technologies that enable effective testing.
In the rapidly evolving world of e-commerce, visual search has become a game-changing technology. Poshmark, a leading fashion resale marketplace, has developed Posh Lens – an advanced visual search engine that revolutionizes how shoppers discover and purchase items.
Under the hood of Posh Lens lies Milvus, a vector database enabling efficient product search and recommendation across our vast catalog of over 150 million items. However, with such an extensive and growing dataset, maintaining high-performance search capabilities while scaling AI infrastructure presents significant challenges.
In this talk, Mahesh Pasupuleti shares:
- The architecture and strategies to scale Milvus effectively within the Posh Lens infrastructure
- Key considerations include optimizing vector indexing, managing data partitioning, and ensuring query efficiency amidst large-scale data growth
- Distributed computing principles and advanced indexing techniques to handle the complexity of Poshmark’s diverse product catalog
As machine learning and deep learning models grow in complexity, AI platform engineers and ML engineers face significant challenges with slow data loading and GPU utilization, often leading to costly investments in high-performance computing (HPC) storage. However, this approach can result in overspending without addressing the core issues of data bottlenecks and infrastructure complexity.
A better approach is adding a data caching layer between compute and storage, like Alluxio, which offers a cost-effective alternative through its innovative data caching strategy. In this webinar, Jingwen will explore how Alluxio's caching solutions optimize AI workloads for performance, user experience and cost-effectiveness.
What you will learn:
- The I/O bottlenecks that slow down data loading in model training
- How Alluxio's data caching strategy optimizes I/O performance for training and GPU utilization, and significantly reduces cloud API costs
- The architecture and key capabilities of Alluxio
- Using Rapid Alluxio Deployer to install Alluxio and run benchmarks in AWS in just 30 minutes
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As more and more companies turn to AI / ML / DL to unlock insight, AI has become this mythical word that adds unnecessary barriers to new adaptors. Oftentimes it was regarded as luxury for those big tech companies only – this should not be the case.
In this talk, Jingwen will first dissect the ML life cycle into five stages – starting from data collection, to data cleansing, model training, model validation, and end at model inference / deployment stages. For each stage, Jingwen will then go over its concept, functionality, characteristics, and use cases to demystify ML operations. Finally, Jingwen will showcase how Alluxio, a virtual data lake, could help simplify each stage.
Alluxio foresaw the need for agility when accessing data across silos separated from compute engines like Spark, Presto, Tensorflow and PyTorch. Embracing the separation of storage from compute, the Alluxio data orchestration platform simplifies adoption of the data lake and data mesh paradigm for analytics and AI/ML. In this talk, Bin Fan will share observations to help identify ways to use the platform to meet the needs of your data environment and workloads.
越來越多的企業架構已轉向混合雲和多雲環境。雖然這種轉變帶來了更大的靈活性和敏捷性，但也意味著必須將計算與存儲分離，這就對企業跨框架、跨雲和跨存儲系統的數據管理和編排提出了新的挑戰。此分享將讓聽眾深入了解Alluxio數據編排理念在數據中台對存儲和計算的解耦作用，以及數據編排針對存算分離場景提出的創新架構，同時結合來自金融、運營商、互聯網等行業的典型應用場景來展現Alluxio如何為大數據計算帶來真正的加速，以及如何將數據編排技術用於AI模型訓練！
*This is a bilingual presentation.
As data stewards and security teams provide broader access to their organization’s data lake environments, having a centralized way to manage fine-grained access policies becomes increasingly important. Alluxio can use Apache Ranger’s centralized access policies in two ways: 1) directly controlling access to virtual paths in the Alluxio virtual file system or 2) enforcing existing access policies for the HDFS under stores. This presentation discusses how the Alluxio virtual filesystem can be integrated with Apache Ranger.
This talk will discuss the process and technical details behind a responsible vulnerability disclosure of an issue detected in Alluxio recently. I will share some of the lessons I’ve learned as a security researcher dealing with multiple open-source vendors and my thoughts about the actions organizations and projects should take to ensure successful vulnerability management and disclosure programs. Learn more about creating more secure software.
Shawn Sun from Alluxio will present the journey of using Alluxio as the storage system for Kubernetes through Container Storage Interface (CSI) plugin and Alluxio CSI driver. This talk will cover the challenges we are facing with traditional setup in the AI/ML training jobs, and how Alluxio CSI driver manages to address them. It will also talk about a recent change to the driver that made it more sturdy and robust.
Shopee is the leading e-commerce platform in SouthEast Asia. In this presentation, Tianbao Ding and Haoning Sun from Shopee will share their Data Infra team’s recent project on acceleration with Presto and storage servitization. They will share the details on how Shopee leverages Alluxio to accelerate Presto query and provide standardized method of accessing data through Alluxio-Fuse and Alluxio-S3.
This presentation will include information about how Alluxio and NetApp StorageGRID helps enterprises accelerate the adoption of cloud and optimize their resource spend on a modern hybrid big data architecture. The conversation will cover use case and architecture info from a variety of enterprises and some of the high level technical details of how these business solutions are constructed.
In this talk, Lei Li and Zifan Ni share the experience of applying Alluxio in their AI platform to increase training efficiency at bilibili. The talk also includes technical architecture and specific issues addressed.
Within Alluxio, the master processes keep track of global metadata for the file system. This includes file system metadata, block cache metadata, and worker metadata. When a client interacts with the filesystem it must first query or update the metadata on the master processes. Given their central role in the system, master processes can be backed by a highly available, fault tolerant replicated journal. This talk will introduce and compare the two available implementations of this journal in Alluxio, the first using Zookeeper and the more recent version using Raft.
Chen Liang from Uber and Beinan Wang from Alluxio will present the practical problems and interesting findings during the launch of Alluxio Local Cache. Their talk covers how Uber’s Presto team implements the cache invalidation and dashboard for Alluxio’s Local Cache. Chen Liang will also share his experience using a customized cache filter to resolve the performance degradation due to a large working set.
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. Alluxio enables you to embrace the separation of storage from compute and use Alluxio data orchestration to simplify adoption of the data lake and data mesh paradigms for analytics and AI/ML workloads.
Join Alluxio’s Sr. Product Mgr., Adit Madan, to learn:
- Key challenges with architecting a successful heterogeneous data platform
- How data orchestration can overcome data access challenges in a distributed, heterogeneous environment
- How to identify ways to use Alluxio to meet the needs of your own data environment and workload requirements
ALLUXIO DAY IX 2022 January 21, 2022 Video: Presentation Slides: The Evolution of an Open Data Platform with Alluxio from Alluxio, Inc.