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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
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Successfully pass the AIF-C01 in the first attempt
Knowledge Check
Question 1: Which AWS service is best suited for running large-scale distributed training of machine learning models?
- Amazon Polly
- AWS Batch
- Amazon SageMaker Training Jobs
- AWS Glue
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