AWS AI Practitioner

What is Generative AI and its Use Cases
How Generative AI Works – Basic Concepts and Terminology Service Offerings in Generative AI from AWS
Large-Language Foundation Models Tokenizers
Embedding Vectors Transformer Architecture
Types of Transformer-Based Foundation Models Pretraining Datasets
Scaling Laws
Compute-Optimal Models Prompts
Completions Tokens
Prompt Engineering Prompt Structure
In-Context Learning with Few-Shot Inference Prompt-Engineering Best Practices Inference Configuration Parameters Machine Learning on AWS cloud platform Amazon SageMaker

Introduction to Amazon Bedrock
How does Amazon Bedrock empower generative AI development?
Key benefits of using Amazon Bedrock for building generative applications. Use cases for generative AI across various industries.
Architecture of Amazon Bedrock Scalability of the Bedrock service
Amazon Bedrock – Inference Parameters – Temperature Other Inference Parameters in Bedrock
Amazon Bedrock’s pricing structure
Exploring options for cost optimization and resource management Overview of AWS CodeWhisperer
Benefits of AWS CodeWhisperer Installing AWS CodeWhisperer Integrating S3 with CodeWhisperer

Use Case Description – Media and Entertainment Industry Use Case Architecture – Amazon Bedrock, Lambda, and S3
Use Case Pre-Requisites – Model Access and Lambda Boto3 Version Upgrade Storing Data in S3 Bucket
Serverless Computing using Lambda
AWS Lambda Integration with Amazon Bedrock Create and maintain APIs using AWS API Gateway AWS API Gateway Integration with Lambda

Use Case Description – Text Summarization Use Case Architecture
Use of Lambda function
AWS SDK for Python (Boto3) upgrade
Connecting Lambda Function with Bedrock Service Use AWS API Gateway for text summarization
Rest API Creation