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AI/ML Infra Meetup May. 23, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Eric Wang (Software Engineer, @Uber) 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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AI/ML Infra Meetup May. 23, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Lu Qiu (Data & AI Platform Tech Lead, @Alluxio) - Siyuan Sheng (Senior Software Engineer, @Alluxio) Speed and efficiency are two requirements for the underlying infrastructure for machine learning model development. Data access can bottleneck end-to-end machine learning pipelines as training data volume grows and when large model files are more commonly used for serving. For instance, data loading can constitute nearly 80% of the total model training time, resulting in less than 30% GPU utilization. Also, loading large model files for deployment to production can be slow because of slow network or storage read operations. These challenges are prevalent when using popular frameworks like PyTorch, Ray, or HuggingFace, paired with cloud object storage solutions like S3 or GCS, or downloading models from the HuggingFace model hub. In this presentation, Lu and Siyuan will offer comprehensive insights into improving speed and GPU utilization for model training and serving. You will learn: - The data loading challenges hindering GPU utilization - The reference architecture for running PyTorch and Ray jobs while reading data from S3, with benchmark results of training ResNet50 and BERT - Real-world examples of boosting model performance and GPU utilization through optimized data access

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AI/ML Infra Meetup May. 23, 2024 Organized by Alluxio For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - Junchen Jiang (Assistant Professor of Computer Science, @University of Chicago) Prefill in LLM inference is known to be resource-intensive, especially for long LLM inputs. While better scheduling can mitigate prefill’s impact, it would be fundamentally better to avoid (most of) prefill. This talk introduces our preliminary effort towards drastically minimizing prefill delay for LLM inputs that naturally reuse text chunks, such as in retrieval-augmented generation. While keeping the KV cache of all text chunks in memory is difficult, we show that it is possible to store them on cheaper yet slower storage. By improving the loading process of the reused KV caches, we can still significantly speed up prefill delay while maintaining the same generation quality.

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In his book, The Nature of the Physical World, Sir Arthur Eddington commented that “We have to appeal to the one outstanding law — the second law of thermodynamics — to put some sense into the world.” This sense-making goes beyond the physical world, too. Entropy is also essential in the fields of information and communication theory. During this lecture for the Princeton Plasma Physics Laboratory, lecturer Andrea Goulet discussed the application of entropy-related concepts in two communication systems: software and collaborative teams. She examined how concepts that help us understand systemic statistical disorder, such as ergodic systems, Lyapunov exponents, Kolmogorov-Sinai entropy, and Shannon-entropy can help us optimize for both software quality and innovation. She also provided several domain-specific models: Lehman’s Laws and Conway’s Law for software, as well as new models from her own research that relate to entropy and innovation. Entropy helps us understand the world and achieve great things. There is an underlying beauty in its principles that we can use to advance scientific discovery. When we understand the subtleties related to balancing surprise and structure, we increase our chances for effective collaboration and finding novel solutions to complex problems.

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Microsoft 365 Copilot is available to Enterprise customers and smaller organizations from January 16, 2024. It goes for the price set at $30 per user/month across all purchasing channels and customer types. Microsoft 365 Copilot gives users experiences allowing for the composition and replying of emails, chats, documents, contacts, and meeting minutes.    Microsoft 365 Copilot is available across all sales channels including Enterprise Agreement and Cloud Solution Provider (CSP).  Also, for commercial customers with:   • Microsoft 365 E3  • Microsoft 365 E5  • Office 365 E3  • Office 365 E5  • Microsoft 365 Business Standard  • Microsoft 365 Business Premium  They will be able to purchase Copilot for Microsoft 365 add-on SKU across all Microsoft sales channels. See below the full list of the Microsoft sales channels for Copilot:  • Enterprise Agreement  • Enterprise Agreement Subscription  • Cloud Solution Provider (CSP)  • Microsoft Customer Agreement – Enterprise (MCA-E)  • Direct from Microsoft  • Education customers through Enrollment for Education Solutions  You can reach out to our team of Microsoft licensing experts concerning any on-premises or cloud related questions seeking for clarity. Send us an email…info@q-advise.com

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Scenarios are the central artifact of the Behaviour Driven Development (BDD) process. Although many teams use scenarios and tools like Cucumber or SpecFlow to automate them, in many cases their scenarios contain a lot of details, particularly test data, and therefore they become too complicated to support collaboration with the business. The "essential" principle of scenario writing (scenario formulation) states that only those details should be included in the scenario that are relevant for the outcome. This talk provides help for those who struggle implementing this principle or would be interested to learn how you can create brief and maintainable scenarios.

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