📚The project focuses on building a generative AI solution using AWS Cloud services.
🔑Key AWS services used include API Gateway, Lambda, and AWS Bedrock for hosting the foundation models.
🔍Different use cases like chatbot, text summarization, and code generation can be implemented using this approach.
🚀AWS SageMaker can be used to deploy and host the foundation models.
🌐The project demonstrates the end-to-end architecture of building and deploying generative AI projects on the AWS Cloud.
What services are used in this generative AI project?
—The key services used are API Gateway, Lambda, and AWS Bedrock. AWS SageMaker can also be used for hosting foundation models.
What use cases can be implemented using this project?
—This project can be used for implementing various use cases like chatbots, text summarization, and code generation.
Can I deploy my own open-source models using this approach?
—Yes, you can deploy both open-source and paid-source models using AWS Bedrock or AWS SageMaker.
Are there any limitations to the number of requests?
—The cost will be based on the number of requests made to the foundation models. AWS Bedrock uses a pay-as-you-go model.
How can I access the generated code or text summaries?
—All the generated code or text summaries are saved in S3 buckets, which can be accessed and retrieved.
00:00In this YouTube video, Krishak introduces a project on building a generative AI solution using AWS Cloud services.
00:39Krishak explains the overall architecture of the project, which involves using AWS services like API Gateway, Lambda, and AWS Bedrock.
04:10The project focuses on solving various use cases like chatbots, text summarization, and code generation.
05:31Krishak discusses the use of AWS SageMaker for deploying and hosting the foundation models.
08:14Krishak demonstrates how to use the project by making an API request and receiving the generated code.
