Uber has numerous deep learning models, most of which are highly complex with many layers and a vast number of features. Understanding how these models work is challenging and demands significant resources to experiment with various training algorithms and feature sets. With ML explainability, the ML team aims to bring transparency to these models, helping to clarify their predictions and behavior. This transparency also assists the operations and legal teams in explaining the reasons behind specific prediction outcomes.
In this talk, Eric Wang will discuss the methods Uber used for explaining deep learning models and how we integrated these methods into the Uber AI Michelangelo ecosystem to support offline explaining.
Uber has numerous deep learning models, most of which are highly complex with many layers and a vast number of features. Understanding how these models work is challenging and demands significant resources to experiment with various training algorithms and feature sets. With ML explainability, the ML team aims to bring transparency to these models, helping to clarify their predictions and behavior. This transparency also assists the operations and legal teams in explaining the reasons behind specific prediction outcomes.
In this talk, Eric Wang will discuss the methods Uber used for explaining deep learning models and how we integrated these methods into the Uber AI Michelangelo ecosystem to support offline explaining.
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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
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- 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