Machine Learning

AWS-DEVGENAI : Developing Generative AI Applications on AWS

Fees

Rs120,000.00

Course Duration

2 Days

Delivery

ILT

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Overview

Expert instructors will guide learners through the foundations of AWS generative AI courses, including the benefits, terminology, and planning steps for generative AI projects. Participants will gain practical skills in prompt engineering and learn to develop advanced applications using AWS services. By the end of the course, you will be equipped to create applications that generate and summarize text, answer questions, and interact with users through chatbot interfaces.

The AWS Generative AI Certification assesses both theoretical knowledge and practical expertise in generative AI. It evaluates candidates on their ability to design, deploy, and optimize generative AI applications across diverse environments. Mastery of AWS generative AI training concepts, including the effective use of Amazon Bedrock, is a critical component of this certification. With hands-on projects and real-world scenarios, the course and certification focus on applying AI models within the AWS generative AI courses framework, ensuring candidates are ready for real-world deployments.

Trainocate’s AWS generative AI courses provide a flexible learning experience, with options for AWS generative AI training and Bedrock training to suit individual schedules. Whether you’re a developer aiming to deepen your skills or a professional exploring the potential of generative AI, these courses offer valuable insights and hands-on expertise to excel in AI-driven projects.

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Course Curriculum

Day 1

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 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

Day 2

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
Demonstration: Invoke 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
Demonstration: Bedrock with LangChain Using a Prompt that Includes Context

Module 8: Architecture Patterns

Introduction to architecture patterns
Text summarization
Demonstration: Text Summarization of Small Files with Anthropic Claude
Demonstration: Abstractive Text Summarization with Amazon Titan Using LangChain
Question answering
Demonstration: Using Amazon Bedrock for Question Answering
Chatbots
Demonstration: Conversational Interface – Chatbot with AI21 LLM
Code generation
Demonstration: Using Amazon Bedrock Models for Code Generation
LangChain and agents for Amazon Bedrock
Demonstration: Integrating Amazon Bedrock Models with LangChain Agents

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What You'll Learn

Describe generative AI and how it aligns to machine learning
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
Explain the steps for planning a generative AI project
Identify some of the risks and mitigations when using generative AI
Understand how Amazon Bedrock works
Familiarize yourself with basic concepts of Amazon Bedrock
Recognize the benefits of Amazon Bedrock
List typical use cases for Amazon Bedrock
Describe the typical architecture associated with an Amazon Bedrock solution
Understand the cost structure of Amazon Bedrock
Implement a demonstration of Amazon Bedrock in the AWS Management Console
Define prompt engineering and apply general best practices when interacting with FMs
Identify the basic types of prompt techniques, including zero-shot and few-shot learning
Apply advanced prompt techniques when necessary for your use case
Identify which prompt-techniques are best-suited for specific models
Identify potential prompt misuses
Analyze potential bias in FM responses and design prompts that mitigate that bias
Identify the components of a generative AI application and how to customize a foundation model (FM)
Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs
Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock
Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications
Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach

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Who Should Attend

This course is intended for:

Software developers interested in leveraging large language models without fine-tuning

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Prerequisites

We recommend that attendees of this course have:

AWS Technical Essentials
Intermediate-level proficiency in Python

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Training Schedule

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Exam & Certification

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Learning Journey

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Frequently Asked Questions

Frequently Asked Questions (FAQs)

Why get AWS certified?

AWS certifications validate your expertise in cloud computing and your proficiency in using AWS services.
These certifications are globally recognized and highly sought after by employers, as they demonstrate your ability to design, deploy, and manage scalable and secure cloud solutions on the AWS platform.
AWS-certified professionals are in high demand, opening doors to new career opportunities and higher earning potential.
What to expect for the examination?

AWS offers a variety of certification exams at different levels (Foundational, Associate, Professional, and Specialty) covering various domains and services.
The exams typically consist of multiple-choice and multiple-response questions, and some may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.
Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.
How long is AWS certification valid for?

AWS certifications are valid for three years from the date of passing the exam.
To maintain your certification, you will need to recertify by passing the latest version of the exam or completing the AWS Cloud Quest game-based training (if option is applicable).
Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.
Why take this course with Trainocate?

Here’s what sets us apart:
- Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 24 countries (and counting!).
- Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.
- Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.
- Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.
- Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.
- Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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Browse All Courses

Overview Course Curriculum Training Schedule Exam & Certification FAQs

What You'll Learn

Describe generative AI and how it aligns to machine learning
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
Explain the steps for planning a generative AI project
Identify some of the risks and mitigations when using generative AI
Understand how Amazon Bedrock works
Familiarize yourself with basic concepts of Amazon Bedrock
Recognize the benefits of Amazon Bedrock
List typical use cases for Amazon Bedrock
Describe the typical architecture associated with an Amazon Bedrock solution
Understand the cost structure of Amazon Bedrock
Implement a demonstration of Amazon Bedrock in the AWS Management Console
Define prompt engineering and apply general best practices when interacting with FMs
Identify the basic types of prompt techniques, including zero-shot and few-shot learning
Apply advanced prompt techniques when necessary for your use case
Identify which prompt-techniques are best-suited for specific models
Identify potential prompt misuses
Analyze potential bias in FM responses and design prompts that mitigate that bias
Identify the components of a generative AI application and how to customize a foundation model (FM)
Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs
Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock
Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications
Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach

Who Should Attend

This course is intended for:

Software developers interested in leveraging large language models without fine-tuning

Prerequisites

We recommend that attendees of this course have:

AWS Technical Essentials
Intermediate-level proficiency in Python

Learning Journey

Coming Soon...

Day 1

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 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

Day 2

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
Demonstration: Invoke 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
Demonstration: Bedrock with LangChain Using a Prompt that Includes Context

Module 8: Architecture Patterns

Introduction to architecture patterns
Text summarization
Demonstration: Text Summarization of Small Files with Anthropic Claude
Demonstration: Abstractive Text Summarization with Amazon Titan Using LangChain
Question answering
Demonstration: Using Amazon Bedrock for Question Answering
Chatbots
Demonstration: Conversational Interface – Chatbot with AI21 LLM
Code generation
Demonstration: Using Amazon Bedrock Models for Code Generation
LangChain and agents for Amazon Bedrock
Demonstration: Integrating Amazon Bedrock Models with LangChain Agents

Frequently Asked Questions (FAQs)

Why get AWS certified?

AWS certifications validate your expertise in cloud computing and your proficiency in using AWS services.
These certifications are globally recognized and highly sought after by employers, as they demonstrate your ability to design, deploy, and manage scalable and secure cloud solutions on the AWS platform.
AWS-certified professionals are in high demand, opening doors to new career opportunities and higher earning potential.
What to expect for the examination?

AWS offers a variety of certification exams at different levels (Foundational, Associate, Professional, and Specialty) covering various domains and services.
The exams typically consist of multiple-choice and multiple-response questions, and some may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.
Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.
How long is AWS certification valid for?

AWS certifications are valid for three years from the date of passing the exam.
To maintain your certification, you will need to recertify by passing the latest version of the exam or completing the AWS Cloud Quest game-based training (if option is applicable).
Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.
Why take this course with Trainocate?

Here’s what sets us apart:
- Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 24 countries (and counting!).
- Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.
- Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.
- Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.
- Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.
- Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.