Artificial Intelligence
Events
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***Please Use Source Link Below to Confirm Event Details*** July's book is 'RAG with Python Cookbook'! This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back. Feel free to join the discussion even if you have not read the book chapters! Join the Flyte MLOps Slack group for discussions during the reading week. Original Event: AI Book Club: RAG with Python Cookbook
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***Please Use Source Link Below to Confirm Event Details*** This week's topic: Coder. Discussion resources to help guide the conversation will be posted below a few days before the meetup. Coder presents a good alternative to pixel-streamed development environments by separating where code runs from how developers interact with it. Instead of streaming full desktop interfaces, developers connect to remote environments using local IDEs such as VS Code or a browser, resulting in a more responsive experience without compromising usability. In this model, code executes on remote, scalable infrastructure while environments are defined and managed as code. This enables teams to standardize development setups and simplify the onboarding of new developers. It also makes it easier to provide controlled access to internal systems and streamline access to pre-approved AI coding agents. We see Coder as a middle ground between local development and fully virtualized desktops: it provides centralized control and governance without the usability limitations of pixel-streamed VDIs. This makes it a good option for organizations that require remote or controlled execution environments, particularly where higher compute or secure access is needed. As with similar approaches, teams should evaluate the operational overhead and security responsibilities that come with managing these environments. Original Event: Discussion - Topic: Coder
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***Please Use Source Link Below to Confirm Event Details*** In this hands-on workshop, you'll use FiftyOne and the High Quality Invoice Images for OCR dataset to run the full data-centric loop end-to-end: embed invoices with a modern visual document model, cluster them by structure, run LightOnOCR as your base model, and use per-sample evaluation scores layered onto embedding space to find where and why it fails. Original Event: Sept 2 - Document Visual AI Workshop
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