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Python ProgrammingDeveloping Generative AI Applications on AWS
Learn to build Generative AI Applications with AWS Services
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Course : Developing Generative AI Applications on AWS
Level : Expert
Type : Live online or Classroom
Length :2 days
This course will guide developers with Python experience through the essentials of generative AI, including its benefits, terminology, and project planning. You’ll explore the foundations of prompt engineering and learn to build applications using AWS services. By the end of the course, you’ll gain the expertise to create applications that generate and summarize text, answer questions, and interact with users through chatbot interfaces.
Software developers
AI Practitioner
Intermediate-level proficiency in Python
Define the importance of generative AI and explain its potential risks and benefits
Identify business value from generative AI use cases
Discuss the technical foundations and key terminology for generative AI
Identify which prompt-techniques are best-suited for specific models,
And more
Not applicable
Instructor-led Virtual online live Training.
Weekend Classes
Evening Classes
Weekdays Classes
Course Outline
Module 1: Introduction to Generative AI - Art of the Possible
Overview of ML
Basics of generative AI
Generative AI use cases
Generative AI in practice
Risks and benefits
Module 2: Planning a Generative AI Project
Generative AI fundamentals
Generative AI in practice
Generative AI context
Steps in planning a generative AI project
Risks and mitigation
Module 3: Getting Started with Amazon Bedrock
Introduction to Amazon Bedrock
Architecture and use cases
How to use Amazon Bedrock
Demonstration: Setting Up Amazon Bedrock Access and Using Playgrounds
Module 4: Foundations of Prompt Engineering
Basics of foundation models
Fundamentals of prompt engineering
Basic prompt techniques
Advanced prompt techniques
Demonstration: Fine-Tuning a Basic Text Prompt
Model-specific prompt techniques
Addressing prompt misuses
Mitigating bias
Demonstration: Image Bias-Mitigation
Module 5: Amazon Bedrock Application Components
Applications and use cases
Overview of generative AI application components
Foundation models and the FM interface
Working with datasets and embeddings
Demonstration: Word Embeddings
Additional application components
RAG
Model fine-tuning
Securing generative AI applications
Generative AI application architecture
Module 6: Amazon Bedrock Foundation Models
Introduction to Amazon Bedrock foundation models
Using Amazon Bedrock FMs for inference
Amazon Bedrock methods
Data protection and auditability
Lab: Invoke Amazon Bedrock model for text generation using zero-shot prompt
Module 7: LangChain
Optimizing LLM performance
Integrating AWS and LangChain
Using models with LangChain
Constructing prompts
Structuring documents with indexes
Storing and retrieving data with memory
Using chains to sequence components
Managing external resources with LangChain agents
Module 8: Architecture Patterns
Introduction to architecture patterns
Text summarization
Lab: Using Amazon Titan Text Premier to summarize text of small files
Lab: Summarize long texts with Amazon Titan
Question answering
Lab: Using Amazon Bedrock for question answering
Chatbots
Lab: Build a chatbot
Code generation
Lab: Using Amazon Bedrock Models for Code Generation
LangChain and agents for Amazon Bedrock
Lab: Building conversational applications with the Converse API
Best Value offer
Official Courseware
Instructor-led live online / Classroom
Flexible Schedule
Course Completion Certification
US$ 1290
Buy now, pay later with PayPal
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Knowledge Check
Question 1: Which AWS service is best suited for running large-scale distributed training of machine learning models?
- Amazon Polly
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- Amazon SageMaker Training Jobs
- AWS Glue
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