On-Demand Videos
Unlock the full performance of your AI/ML infrastructure on Oracle Cloud Infrastructure (OCI).
Join Oracle's Master Principal Cloud Architect Xinghong He and Alluxio's VP of Technology Bin Fan for an in-depth technical session exploring how modern tiered caching, optimized storage integration, and smart deployment choices can deliver sub-millisecond latency and up to 5× faster data access on OCI — at scale.
You'll learn about:
- Architectural insights: How Alluxio’s tiered caching architecture works with OCI Object Storage and BM.DenseIO compute instances to eliminate data access bottlenecks.
- Benchmark-proven results: See real MLPerf Storage 2.0 and Warp benchmark outcomes demonstrating sub-millisecond latency and dramatic throughput gains.
- Deployment strategies: Compare deployment options — dedicated mode for peak performance vs. co-located mode for cost-efficient scale.
- Practical, actionable guidance: Implementation best practices you can apply directly to your AI/ML workloads on OCI.
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
In this talk, Eric Wang, Senior Staff Software Engineer introduces Uber’s open-source generative end-to-end ML lifecycle management platform: Michelangelo.
.png)
In November’s Product School, Adit Madan, Director of Product Management at Alluxio, will highlights new features, enhanced manageability, improved security and performance in Alluxio 2.9 release.
Big Data Bellevue Meetup
May 19, 2022
Today, data engineering in modern enterprises has become increasingly more complex and resource-consuming, particularly because (1) the rich amount of organizational data is often distributed across data centers, cloud regions, or even cloud providers, and (2) the complexity of the big data stack has been quickly increasing over the past few years with an explosion in big-data analytics and machine-learning engines (like MapReduce, Hive, Spark, Presto, Tensorflow, PyTorch to name a few).
To address these challenges, it is critical to provide a single and logical namespace to federate different storage services, on-prem or cloud-native, to abstract away the data heterogeneity, while providing data locality to improve the computation performance. [Bin Fan] will share his observation and lessons learned in designing, architecting, and implementing such a system – Alluxio open-source project — since 2015.
Alluxio originated from UC Berkeley AMPLab (used to be called Tachyon) and was initially proposed as a daemon service to enable Spark to share RDDs across jobs for performance and fault tolerance. Today, it has become a general-purpose, high-performance, and highly available distributed file system to provide generic data service to abstract away complexity in data and I/O. Many companies and organizations today like Uber, Meta, Tencent, Tiktok, Shopee are using Alluxio in production, as a building block in their data platform to create a data abstraction and access layer. We will talk about the journey of this open source project, especially in its design challenges in tiered metadata storage (based on RocksDB), embedded state-replicate machine (based on RAFT) for HA, and evolution in RPC framework (based on gRPC) and etc.
Meetup Group
Big Data Bellevue: https://www.meetup.com/big-data-bellevue-bdb/
Big Data Bellevue & Cloudy With a Chance of Data Meetup
October 20, 2022
Distributed systems are made up of many components such as authentication, a persistence layer, stateless services, load balancers, and stateful coordination services. These coordination services are central to the operation of the system, performing tasks such as maintaining system configuration state, ensuring service availability, name resolution, and storing other system metadata. Given their central role in the system it is essential that these systems remain available, fault tolerant and consistent. By providing a highly available file system-like abstraction as well as powerful recipes such as leader election, Apache Zookeeper is often used to implement these services. This talk will go over a generic example of stateful coordination service moving from Zookeeper to Raft.
Meetup Groups
Big Data Bellevue: https://www.meetup.com/big-data-bellevue-bdb/
Cloudy With a Chance of Data: https://www.meetup.com/meetup-datascience/
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.
ALLUXIO DAY x APAC Modern Data Stack 2022
In this presentation, Yingjun Wu, Founder @ RisingWave Labs will talk about the birth, the growth, and the prosperity of modern data stack. I will show you why modern data stack is more than a buzzword, and how it will possibly evolve in the next couple of years.
ALLUXIO DAY x APAC Modern Data Stack 2022
September 22, 2022
Apache Hudi’s open-source community is very active and healthy. In this talk, an overview of community-driven major features will be presented, followed by a deep-dive into two of those features, metastore and table management service, driven by Bytedance to illustrate Hudi’s platform vision.
ALLUXIO DAY x APAC Modern Data Stack 2022
September 22, 2022
Shopee is the leading e-commerce platform in SouthEast Asia. In this presentation, Luo Li from Shopee will share their Data Infra team’s recent project on acceleration with Presto and storage servitization. He will share the details on how Shopee leverages Alluxio to accelerate Presto query and provide standardized methods of accessing data through Alluxio-Fuse and Alluxio-S3.
ALLUXIO DAY XV 2022
September 15, 2022
OceanBase Database, is an open-source, distributed Hybrid Transactional/Real-time Operational Analytics (HTAP) database management system that has set new world records in both the TPC-C and TPC-H benchmark tests. OceanBase Database starts from 2010, and it has been serving all of the critical systems in Alipay. Besides Alipay, OceanBase has also been serving customer from a variety of sectors, including Internet, financial services, telecommunications and retail industry.
In this tech talk, we will talk about the architecture of OceanBase and some typical use cases. This talk will include some technical topic such as Paxos replication, 2PC commit, LSM-Tree like storage, SQL optimizer and executor, city-level disaster recovery, etc.
ALLUXIO DAY XV 2022
September 15, 2022
This talk introduces the three game level progressions to use Alluxio to speed up your cloud training with production use cases from Microsoft, Alibaba, and BossZhipin.
- Level 1: Speed up data ingestion from cloud storage
- Level 2: Speed up data preprocessing and training workloads
- Level 3: Speed up full training workloads with a unified data orchestration layer
ALLUXIO DAY XV 2022
September 15, 2022
With the advent of the Big Data era, it is usually computationally expensive to calculate the resource usages of a SQL query. Can we estimate the resource usages of SQL queries more efficiently without any computation in a SQL engine kernel? In this session, Chunxu and Beinan would like to introduce how Twitter’s data platform leverages a machine learning-based approach in Presto and BigQuery to estimate query utilization with 90%+ accuracy.
ALLUXIO DAY XV 2022
September 15, 2022
Streaming systems form the backbone of the modern data pipeline as the stream processing capabilities provide insights on events as they arrive. But what if we want to go further than this and execute analytical queries on this real-time data? That’s where Apache Pinot comes in.
OLAP databases used for analytical workloads traditionally executed queries on yesterday’s data with query latency in the 10s of seconds. The emergence of real-time analytics has changed all this and the expectation is that we should now be able to run thousands of queries per second on fresh data with query latencies typically seen on OLTP databases.
Apache Pinot is a realtime distributed OLAP datastore, which is used to deliver scalable real time analytics with low latency. It can ingest data from streaming sources like Kafka, as well as from batch data sources (S3, HDFS, Azure Data Lake, Google Cloud Storage), and provides a layer of indexing techniques that can be used to maximize the performance of queries.
Come to this talk to learn how you can add real-time analytics capability to your data pipeline.
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.