Скачать или смотреть Question Answer demo using LLM on AWS SageMaker

In this video, we explore how to build a powerful Question Answer (QA) application using Generative AI on AWS SageMaker. By leveraging the capabilities of AWS services, we demonstrate the process step-by-step.

First, we start by feeding the data from the S3 bucket into our application. To achieve this, we utilize the Glue crawler, which enables us to extract data from various sources such as GCP, Snowflake, RDS, Redshift, and more.

Once the data is ingested, we proceed to combine these datasets within our QA application. We employ the RAG Langchain framework, a cutting-edge approach that allows us to generate answers by leveraging the power of Generative AI.

Throughout the video, we provide detailed instructions and insights on how to implement each step of the process. By following along, you will gain a solid understanding of how to leverage AWS SageMaker to build your own QA application and harness the capabilities of Generative AI.

Whether you are a developer, data scientist, or AI enthusiast, this video will equip you with the knowledge and skills needed to create powerful QA applications using Generative AI on AWS SageMaker. Watch now and unlock the potential of AI-driven question-answering!

Timestamps:
0:00 - Demo Title
0:20 - Introduction of Demo
3:35 - SageMaker Introduction
4:00 - SageMaker notebook Instance
4:50 - Create Glue Role
9:19 - Create a Glue Crawler
12:27 - Feeding data from S3 bucket using Glue crawler
14:15 - Define Prompt Templates
15:55 - Define LLM
17:30 - Introduction to RAG Langchain framework
20:35 - Generating answers using Generative AI
23:20 - Conclusion and next steps

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🧑‍💻 Git: https://github.com/chicks2014/Questio...


✨ Tags ✨
LLM QA LangChain GenAI

✨ Hashtags ✨
#qa #llm #genai #generativemodels #chatgpt4

Note: This video assumes basic familiarity with AWS services and AI concepts. Prior knowledge of AWS SageMaker and Generative AI will be helpful but not mandatory.