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
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
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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