In this AWS Bedrock course, gives a foundational understanding of Generative AI and how foundation models (FMs) can be used to solve problems like text summarization, semantic/conversational search, and content generation. Using libraries like Python boto3, LangChain, and Streamlit, you will find out how to build Generative AI applications that leverage Foundational Large Language Models (LLMs) using APIs without managing infrastructure. Explore the pre-built FMs available in Amazon Bedrock and the features that Amazon Bedrock provides. Plus, practice what you learn as you build a basic Gen AI application using Amazon Bedrock to demonstrate its capabilities.
Course Objectives:
In this course, you will learn to:
- Gain insights into AWS Bedrock and LLMs, positioning yourself at the forefront of AI innovation
- Build a theoretical and practical foundation in AI, encompassing everything from LLMs to Diffusion Models
- Apply knowledge through projects like chatting with data, coding with foundational models, and image generation
- Acquire skills in serverless architecture integration and deployment using AWS services including AWS Bedrock, API Gateway, Lambda functions, S3, Postman, and more
- Learn to fine-tune and evaluate LLMs for ethical and effective AI solutions
- Position yourself as a valuable asset in the evolving AI industry
- Get hands-on experience with AWS services, directly transferable to professional settings
- Develop critical thinking and problem-solving skills in AI project design and execution, preparing you for complex challenges in the tech field
Course content
Introduction
- Learning Amazon Bedrock overview
- What you should know
- AWS setup
- Set up AWS credentials
Amazon Bedrock Introduction
- What is generative AI?
- What is Amazon Bedrock?
- Models in Amazon Bedrock
- Using Playgrounds for Amazon Bedrock
Amazon Bedrock API Integration
- Amazon Bedrock API documentation
- Python Boto3 Amazon Bedrock support
- Invoking Amazon Bedrock APIs
- Introduction to LangChain
- Simple LangChain workflow
Building a GenAI Application
- What is RAG architecture?
- Amazon Bedrock use case
- RAG application approach
- Coding the solution
- Running the solution
- Adding Streamlit integration
- Running the completed application
Conclusion
- Clean-up for Amazon Bedrock
- Amazon Bedrock application enhancements
To see the full course content Download now
Course Prerequisites
- Basic Python knowledge
- AWS Account
Who can attend
- Anyone who is familiar with Python, AWS services, and wants to learn to quickly deploy LLM apps with Amazon Bedrock.
Number of Hours: 25hrs
Certification
Key features
- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Virtual Live Experience
- Preparing for Certification
FAQs
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- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Materials
- Preparing for Certification
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