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Zenlytic Is Building You A Better Coworker With AI Agents

Data Engineering Podcast

Summary The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. Are data agents harder to build?

Building 278
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Building a Recommendation System with Hugging Face Transformers

KDnuggets

Learn how to build the recommendation system with advanced technology.

Systems 141
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Find Out About The Technology Behind The Latest PFAD In Analytical Database Development

Data Engineering Podcast

Summary Building a database engine requires a substantial amount of engineering effort and time investment. Over the decades of research and development into building these software systems there are a number of common components that are shared across implementations. Your first 30 days are free!

Database 162
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Building Linked Data Products With JSON-LD

Data Engineering Podcast

Summary A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. What is the level of native support/compatibiliity that you see for JSON-LD in data systems?

Building 189
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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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Data Migration Strategies For Large Scale Systems

Data Engineering Podcast

Summary Any software system that survives long enough will require some form of migration or evolution. When that system is responsible for the data layer the process becomes more challenging. As you have gone through successive migration projects, how has that influenced the ways that you think about architecting data systems?

Systems 130
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Building Meta’s GenAI Infrastructure

Engineering at Meta

By the end of 2024, we’re aiming to continue to grow our infrastructure build-out that will include 350,000 NVIDIA H100 GPUs as part of a portfolio that will feature compute power equivalent to nearly 600,000 H100s. RSC has accelerated our open and responsible AI research by helping us build our first generation of advanced AI models.

Building 145
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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.