Скачать или смотреть Integrating Structured and Unstructured Data for Better AI Solutions

Understanding the role of structured and unstructured data is crucial. This video delves into how these two data types interact within AI pipelines.

Structured data, characterized by its organized format, is typically stored in databases and is easily searchable. In contrast, unstructured data encompasses a variety of formats, including text documents, images, and multimedia files, which are often more challenging to analyze. The integration of both data types can enhance AI performance significantly.

By utilizing retrieval-augmented generation (RAG) pipelines, organizations can leverage the strengths of both structured and unstructured data, leading to improved decision-making processes.

The video covers real-world examples of how enterprises are currently implementing these strategies. It also discusses the challenges and opportunities presented by combining these data types, providing viewers with insights into best practices and methodologies for effective AI data development.
Timestamps:
00:00 Introduction
01:36 Agenda Overview
02:41 AI Co-Pilots and Assistants
04:01 Knowledge Transfer in AI
04:40 AI Models and Their Components
06:07 Evaluation of AI Responses
07:21 Labeling Functions
08:45 Snorkel Flow Overview
10:49 RAG Pipelines Discussion
13:22 Genetic RAG Concepts
16:56 Enterprise AI Perspective
17:02 Introduction to Number Station
17:41 Problem Overview
19:05 Number Station Solution Overview
21:04 Multi-Agent Framework
23:26 Summary of Number Station
24:07 Technical Deep Dive Introduction
24:22 Building an Analytics Agent
25:54 Agentic Systems Explanation
27:12 RAG and Accuracy
30:04 Combining Unstructured and Structured Data
32:23 Demo of Agents in Action
36:29 Q&A Session
50:03 Closing Remarks

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