Abstract
Date
Wednesday Apr 9 / 01:35PM BST ( 50 minutes )
Location
Fleming (3rd Fl.)
Slides
Slides are not available
Panel: Modern Data Architectures
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Session
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Reliable Data Flows and Scalable Platforms: Tackling Key Data Challenges
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There are a few common and mostly well-known challenges when architecting for data. For example, many data teams struggle to move data in a stable and reliable way from operational systems to analytics systems.
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Achieving Precision in AI: Retrieving the Right Data Using AI Agents
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In the race to harness the power of generative AI, organizations are discovering a hidden challenge: precision.
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Beyond the Warehouse: Why BigQuery Alone Won’t Solve Your Data Problems
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Many organizations mistake the adoption of a data warehouse, like BigQuery, as the golden ticket to solving all their data challenges. But without a robust data strategy and architecture, you’re simply shifting chaos into the cloud.
Sarah Usher
Data & Backend Engineer, Community Director, Mentor
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The Data Backbone of LLM Systems
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Any LLM application has four dimensions you must carefully engineer: the code, data, models and prompts. Each dimension influences the other. That's why you must learn how to track and manage each. The trick is that every dimension has particularities requiring unique strategies and tooling.
Paul Iusztin
Senior ML/AI Engineer, MLOps, Founder @Decoding ML