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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

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In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution. RAG with semantic search – Conventional RAG with semantic search was the last step before moving to SQL generation.

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Adopting & Scaling AI, a Beginner’s Guide to Prompt Engineering, and Pretraining Large Language…

ODSC - Open Data Science

Choosing a Data Lake Format: What to Actually Look For The differences between many data lake products today might not matter as much as you think. When choosing a data lake, here’s something else to consider. Use this guide to get started with your prompt engineering skills! Register now for 60% off.

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The Importance of Domain-Specific LLMs, Jobs in Prompt Engineering, and Our Data Primer Series

ODSC - Open Data Science

Accelerating Decisions with Third-Party Data in Financial Services On-Demand Webinar Your ability to make confident decisions based on relevant factors relies on accurate data filled with context. That’s why enriching your analysis with trusted, fit-for-use, third-party data is key to ensuring long-term success.

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Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a…

ODSC - Open Data Science

5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists Between its ability to perform data analysis and ease-of-use, here are 5 reasons why SQL is still ideal for new data scientists to get into the field.

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Your Complete Roadmap to Become an Azure Data Scientist

Pickl AI

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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