Presto: Fast SQL-on-Anything Across Data Lakes, DBMS, and NoSQL Data Stores

Alluxio Webinar April 6, 2021 For more Alluxio events: https://www.alluxio.io/events/ Speakers: Alex Ma, Alluxio Peter Behrakis, Alluxio Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows. In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see. In this tech talk, we'll go over: - What is Alluxio Data Orchestration? - How does it work? - Alluxio customer results

Accelerate Analytics and ML in the Hybrid Cloud Era

Orchestrate a Data Symphony

Data Orchestration Summit 2020 organized by Alluxio https://www.alluxio.io/data-orchestration-summit-2020/ Exploring Alluxio for Daily Tasks at Robinhood Jiawei Zhang, Data Platform Engineer (Robinhood) Yichuan Huang, Data Platform Engineer (Robinhood) Grace Lu, Data Platform Engineer (Robinhood) Wenlong Xiong, Data Platform Engineer (Robinhood) About Alluxio: alluxio.io Engage with the open source community on slack: alluxio.io/slack

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Alluxio Global Online Meetup August 25, 2020 For more Alluxio events: https://www.alluxio.io/events/ Speakers: Abner Ferreira, Simbiose Ventures Caio Pavanelli, Simbiose Ventures Bin Fan, Alluxio Over the last few years, organizations have worked towards the separation of storage and compute for a number of benefits in the areas of cost, data duplication and data latency. Cloud resolves most of these issues but comes to the expense of needing a way to query data on remote storages. Alluxio and Presto are a powerful combination to address the compute problem, which is part of the strategy used by Simbiose Ventures to create a product called StorageQuery - A platform to query files in cloud storages with SQL. This talk will focus on: - How Alluxio fits StorageQuery's tech stack; - Advantages of using Alluxio as a cache layer and its unified filesystem; - Development of new under file system for Backblaze B2 and fine-grained code documentation; - ShannonDB remote storage mode.

StorageQuery: federated querying on object stores, powered by Alluxio and Presto

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Alluxio Global Online Meetup August 4, 2020 For more Alluxio events: https://www.alluxio.io/events/ Speakers: Koen Michiels, Datasapiens Juraj Pohanka, Datasapiens Bin Fan, Alluxio Datasapiens is an international data-analytics startup based in Prague. We help our clients to uncover the value of their data and open up new revenue streams for them. We provide an end-to-end service that manages the data pipeline and automates the process of generating data insights. In this talk, we will describe how we have solved an issue with large S3 API costs incurred by Presto under several usage concurrency levels by implementing Alluxio as a data orchestration layer between S3 and Presto. Also, we will show the results of an experiment with estimating the per-query S3 API costs using the TPC-DS dataset. This talk will focus on: - The Hadoop ecosystem at Datasapiens - Drastic increase of S3 API costs during performance tests with Presto - S3 API costs tests with TPC-DS - Implications to the cloud data lake architecture

Reducing large S3 API costs using Alluxio at Datasapiens

Alluxio + Spark: Accelerating Auto Data Tagging in WeRide

Alluxio Tech Talk January 21, 2020 Speakers: Matt Fuller, Starburst Dipti Borkar, Alluxio With the advent of the public clouds and data increasingly siloed across many locations -- on premises and in the public cloud -- enterprises are looking for more flexibility and higher performance approaches to analyze their structured data. Join us for this tech talk where we’ll introduce the Starburst Presto, Alluxio, and cloud object store stack for building a highly-concurrent and low-latency analytics platform. This stack provides a strong solution to run fast SQL across multiple storage systems including HDFS, S3, and others in public cloud, hybrid cloud, and multi-cloud environments. You’ll learn more about: - The architecture of Presto, an open source distributed SQL engine - How the Presto + Alluxio stack queries data from cloud object storage like S3 for faster and more cost-effective analytics - Achieving data locality and cross-job caching with Alluxio regardless of where data is persisted

Accelerating analytics in the cloud with the Starburst Presto + Alluxio stack

Alluxio Global Online Meetup Apr 23, 2020 For more Alluxio events: https://www.alluxio.io/events/ Speakers: Jiao (Jennie) Wang, Intel Tsai Louie, Intel Bin Fan, Alluxio Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked. Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications. This talk, we will go over: - What is Analytics Zoo and how it works - How to run Analytics Zoo with Alluxio in deep learning applications - Initial performance benchmark results using the Analytics Zoo + Alluxio stack

Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio

Alluxio Community Office Hour July 14, 2020 For more Alluxio events: https://www.alluxio.io/events/ Speakers: Calvin Jia, Alluxio Bin Fan, Alluxio Alluxio 2.3 was just released at the end of June 2020. Calvin and Bin will go over the new features and integrations available and share learnings from the community. Any questions about the release and on-going community feature development are welcome. In this Office Hour, we will go over: - Glue Under Database integration - Under Filesystem mount wizard - Tiered Storage Enhancements - Concurrent Metadata Sync - Delegated Journal Backups

What's New in Alluxio 2.3

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Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.

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Alluxio Use Cases and Future Directions

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3 Things to Learn: -How data is driving digital transformation to help businesses innovate rapidly -How Choice Hotels (one of largest hoteliers) is using Cloudera Enterprise to gain meaningful insights that drive their business -How Choice Hotels has transformed business through innovative use of Apache Hadoop, Cloudera Enterprise, and deployment in the cloud — from developing customer experiences to meeting IT compliance requirements

How Data Drives Business at Choice Hotels

Cloudera, Inc.

The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies. However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business. In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.

How to Build Continuous Ingestion for the Internet of Things

Cloudera, Inc.

Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake. Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture. Dipti will cover: -Open Data Lake analytics - what it is and what use cases it supports -Why companies are moving to an open data lake analytics approach -Why the open source data lake query engine Presto is critical to this approach

Unlocking the Value of Your Data Lake

DATAVERSITY

Cloud-native Semantic Layer on Data Lake

Apache Spark 2.0 set the architectural foundations of Structure in Spark, Unified high-level APIs, Structured Streaming, and the underlying performant components like Catalyst Optimizer and Tungsten Engine. Since then the Spark community has continued to build new features and fix numerous issues in releases Spark 2.1 and 2.2. Continuing forward in that spirit, the upcoming release of Apache Spark 2.3 has made similar strides too, introducing new features and resolving over 1300 JIRA issues. In this talk, we want to share with the community some salient aspects of soon to be released Spark 2.3 features: • Kubernetes Scheduler Backend • PySpark Performance and Enhancements • Continuous Structured Streaming Processing • DataSource v2 APIs • Structured Streaming v2 APIs

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Element Fleet has the largest benchmark database in our industry and we needed a robust and linearly scalable platform to turn this data into actionable insights for our customers. The platform needed to support advanced analytics, streaming data sets, and traditional business intelligence use cases. In this presentation, we will discuss how we built a single, unified platform for both Advanced Analytics and traditional Business Intelligence using Cassandra on DSE. With Cassandra as our foundation, we are able to plug in the appropriate technology to meet varied use cases. The platform we’ve built supports real-time streaming (Spark Streaming/Kafka), batch and streaming analytics (PySpark, Spark Streaming), and traditional BI/data warehousing (C*/FiloDB). In this talk, we are going to explore the entire tech stack and the challenges we faced trying support the above use cases. We will specifically discuss how we ingest and analyze IoT (vehicle telematics data) in real-time and batch, combine data from multiple data sources into to single data model, and support standardized and ah-hoc reporting requirements. About the Speaker Jim Peregord Vice President - Analytics, Business Intelligence, Data Management, Element Corp.

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DataStax

IBM Cloud Day January 2021 - A well architected data lake

Torsten Steinbach

Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization. Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support. In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.

Data Warehouse or Data Lake, Which Do I Choose?

DATAVERSITY

This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA. We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that). But what is a DWaaS really? How is it different from traditional on-premises data warehousing? In this talk I will: • Demystify DWaaS by defining it and its goals • Discuss the real-world benefits of DWaaS • Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.

Demystifying Data Warehouse as a Service (DWaaS)

Kent Graziano

Presented at Global AI Conference in Boston 2018: http://www.globalbigdataconference.com/boston/global-artificial-intelligence-conference-106/speaker-details/kamil-bajda-pawlikowski-62952.html Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Facebook, Airbnb, Netflix, Uber, Twitter, LinkedIn, Bloomberg, and FINRA, Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments in the last few years. Presto is really a SQL-on-Anything engine in a single query can access data from Hadoop, S3-compatible object stores, RDBMS, NoSQL and custom data stores. This talk will cover some of the best use cases for Presto, recent advancements in the project such as Cost-Based Optimizer and Geospatial functions as well as discuss the roadmap going forward.

Presto talk @ Global AI conference 2018 Boston

kbajda

The effective use of big data is the key to gaining a competitive advantage and outperforming the competition. This change demands that companies consume and blend enormous amount of data created from divergent and inherently mismatched sources, which represents a paradigm shift to the traditional data warehouse. Companies need to modernize their data warehouse, augmenting it with a platform that allows storage, processing, exploration and analysis of large and diverse datasets without limiting the ability to deliver the data access, and flexibility responding to the needs of the business. That’s where Oracle Cloud and Qubole work together delivering a new breed of data platform —capable of storing and processing the overwhelming amount of data that on-premises big data deployments cannot handle. Watch this on-demand webinar to understand: - Why deploying big data on-premises is expensive, complex to maintain and limits your ability to scale across new use cases and data sources - How Oracle Bare Metal Cloud's predictable and fast performance compute and network services deliver the foundation of a cost-effective, high-performance big data platform - How Qubole leverages Oracle Bare Metal Cloud to provide a turnkey big data service that optimizes cost, performance, and scale, enabling self-service data exploration. Qubole delivers a cloud-based, turnkey, self-service big data service that removes the complexity and reduces the cost of doing big data. It leverages Oracle Bare Metal Cloud’s next generation of scalable, inexpensive and performant compute, network and storage public cloud infrastructure to provide a solution that accelerates time to market and reduces the risk of your big data initiatives.

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Qubole

2018 02-08-what's-new-in-apache-spark-2.3

Chester Chen

In this session you will learn how Qlik’s Data Integration platform (formerly Attunity) reduces time to market and time to insights for modern data architectures through real-time automated pipelines for data warehouse and data lake initiatives. Hear how pipeline automation has impacted large financial services organizations ability to rapidly deliver value and see how to build an automated near real-time pipeline to efficiently load and transform data into a Snowflake data warehouse on AWS in under 10 minutes.

Accelerate and modernize your data pipelines

Paul Van Siclen

Big data journey to the cloud 5.30.18 asher bartch

Cloudera, Inc.

Progressive Insurance is well known for its innovative use of data to better serve its customers, and the important role that Hortonworks Data Platform has played in that transformation. However, as with most things worth doing, the path to the Data Lake was not without its challenges. In this session, I’ll share our top use cases for Hadoop – including telematics and display ads, how a skills shortage turned supporting these applications into a nightmare, and how – and why – we now use Syncsort DMX-h to accelerate enterprise adoption by making it quick and easy (or faster and easier) to populate the data lake – and keep it up to date – with data from across the enterprise. I’ll discuss the different approaches we tried, the benefits of using a tool vs. open source, and how we created our Hadoop Ingestor app using Syncsort DMX-h.

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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

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Alluxio x Tobiko - ETL Happy Hour April 16, 2024 For more Alluxio events: https://alluxio.io/events/ Speaker: Toby Mao (CTO @ Tobiko Data) Writing efficient and correct incremental pipelines is challenging. Data practitioners who take on this challenge are viewed as performing an "advanced" function, which discourages broader teams from adopting incremental loads. In this lightning talk, CTO of Tobiko Data, Toby Mao, will demystify incremental loading data at scale.

Correctly Loading Incremental Data at Scale

Big Data Bellevue Meetup March 21, 2024 For more Alluxio events: https://alluxio.io/events/ Speakers: Bin Fan (VP of Open Source, Alluxio) 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.

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Alluxio Monthly Webinar Feb. 27, 2024 For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Tarik Bennett (Senior Solutions Engineer, Alluxio) 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

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Alluxio Monthly Webinar Jan. 30, 2024 For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Kevin Petrie (VP of Research, Eckerson Group) - Omid Razavi (SVP of Customer Success, Alluxio) 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

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Data Infra Meetup Jan. 25, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Juncheng Yang(Ph.D Candidate, @CMU) 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, I will describe 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.

Data Infra Meetup | FIFO Queues are All You Need for Cache Eviction

Data Infra Meetup Jan. 25, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Jingwen Ouyang (Product Manager, @Alluxio) 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.

Data Infra Meetup | Accelerate Your Trino/Presto Queries - Gain the Alluxio Edge

Data Infra Meetup Jan. 25, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Siyuan Sheng (Senior Software Engineer, @Alluxio) - Chunxu Tang (Research Scientist, @Alluxio) 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.

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Data Infra Meetup | ByteDance's Native Parquet Reader

Data Infra Meetup Jan. 25, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Jing Zhao (Principal Engineer, @Uber) 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

Data Infra Meetup | Uber's Data Storage Evolution

Alluxio Monthly Webinar Nov. 15, 2023 For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Tarik Bennett (Senior Solutions Engineer) - Beinan Wang (Senior Staff Engineer & Architect) 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: 1) Optimizing a developmental setup can include manual copies, which are slow and error-prone 2) 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

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AI Infra Day Oct. 25, 2023 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Adit Madan (Director of Product Management, @Alluxio) In this session, Adit Madan, Director of Product Management at Alluxio, presents an overview of using distributed caching to accelerate model training and serving. He explores the requirements of data access patterns in the ML pipeline and offers practical best practices for using distributed caching in the cloud. This session features insights from real-world examples, such as AliPay, Zhihu, and more.

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AI Infra Day Oct. 25, 2023 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Bin Fan (Cheif Architect, VP of Open Source, @Alluxio) As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for a robust AI infra stack. This opening keynote will explore the key trends of the AI infra stack in the generative AI era.

AI Infra Day | The AI Infra in the Generative AI Era

AI Infra Day Oct. 25, 2023 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Lu Qiu (Machine Learning Engineer, @Alluxio) - Shawn Sun (Software Engineer, @Alluxio) This hands-on session will discuss best practices for using PyTorch and Alluxio during model training on AWS. Chunxu and Lu will provide a step-by-step demonstration of how to use Alluxio on EKS as a distributed cache to accelerate computer vision model training jobs that read datasets from S3. This architecture significantly improves the utilization of GPUs from 30% to 90%+, archives ~5x faster training, and lower cloud storage costs.

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AI Infra Day Oct. 25, 2023 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Jordan Plawner (Global Director of Artificial intelligence Product Management and Strategy, @Intel) ChatGPT and other massive models represents an amazing step forward in AI, yet they do not solve real-world business problems. We will survey how the AI ecosystem has worked non-stop over this last year to take these all-purpose multi-task models and optimize them to they can be used by organizations to address domain specific problems. We will explain these new AI-for-the-real world techniques and methods such as fine tuning and how can be applied to deliver results which are highly performant with state-of-the-art accuracy while also being economical to build and deploy everywhere to enhance products and services.

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AI Infra Day Oct. 25, 2023 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Sally (Mihyoung) Lee (Senior Staff Engineer, TLM, @Uber) Machine learning models power Uber’s everyday business. However, developing and deploying a model is not a one-time event but a continuous process that requires careful planning, execution, and monitoring. This session will highlight Uber’s practice on the machine learning lifecycle to ensure high model quality.

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Alluxio Monthly Webinar Oct. 3, 2023 For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Greg Palmer (Lead Solutions Engineer) Model training requires extensive computational and GPU resources. When training models on AWS, loading data from S3 often becomes a major bottleneck, wasting valuable GPU cycles. Optimizing data loading can greatly reduce GPU idle time and increase GPU utilization. In this webinar, Greg Palmer will discuss best practices for efficient data loading during model training on AWS. He will demonstrate how to use Alluxio on EKS as a distributed cache to accelerate PyTorch training jobs that read datasets from S3. This architecture significantly improves the utilization of GPUs from 30% to 90%+, archives ~5x faster training, and lower cloud storage costs. What you will learn: - The challenges of feeding data-hungry GPUs in the cloud - How to accelerate model training by optimizing data loading on AWS - The reference architecture for running PyTorch jobs with Alluxio cache on EKS while reading data from S3, with benchmark results of training ResNet50 and BERT - How to use TensorBoard to identify bottlenecks in GPU utilization

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