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Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
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Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
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In this presentation, we delve into leveraging Amazon Q to elevate developer efficiency and craft GenAI applications. Discover the key features and benefits of Amazon Q for streamlined application development. Learn how Amazon Q can revolutionize your development processes and empower you to create cutting-edge GenAI applications.
Elevate Developer Efficiency & build GenAI Application with Amazon Q
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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.
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WSO2's API Vision: Unifying Control, Empowering Developers
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Process management Memory
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