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Program Highlights

Master the essential tools and techniques for creating innovative AI applications on AWS. Developing Generative AI Applications on AWS course equips participants with the practical skills and expertise to develop, deploy, and manage AI-driven solutions. Ideal for AI professionals and developers, it offers hands-on experience and insights into building impactful AI applications that harness the power of AWS technologies. Whether you’re advancing your career or expanding your skill set, this course prepares you to excel in the rapidly evolving field of Generative AI.

  • 16-Hour of Instructor-led Training16-Hour of Instructor-led Training
  • Practical Examples from Real-world Case StudiesPractical Examples from Real-world Case Studies
  • Learn with Real-World ScenariosLearn with Real-World Scenarios
  • Highly Interactive and Dynamic SessionsHighly Interactive and Dynamic Sessions
  • Immersive LearningImmersive Learning
  • Learn from Industry ExpertsLearn from Industry Experts
  • Career Guidance and MentorshipCareer Guidance and Mentorship
  • Extended Post Training SupportExtended Post Training Support
  • Access to Recorded SessionsAccess to Recorded Sessions

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About Course

Developing Generative AI Applications on AWS training course from InfosecTrain is a hands-on course designed to equip participants with the skills to build AI-powered applications using Amazon Web Services. Participants will start by understanding the basics of Generative AI and Machine Learning, then dive into practical topics like prompt engineering, architecture patterns, and using tools such as Amazon Bedrock and LangChain.

The course covers key areas, including planning AI projects, fine-tuning models, and securing your applications. With a blend of theory and hands-on demonstrations, participants will learn how to create and optimize AI solutions for tasks like text generation, chatbot development, and more. By the end of the course, participants will be ready to develop impactful AI applications that leverage the full capabilities of AWS.

Course Curriculum

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

 

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

Target Audience
  • Data Scientists
  • Machine Learning Engineers
  • Software Developers
  • Innovation Teams in Enterprises
  • IT Professionals
Pre-requisites
  • A foundational grasp of how machine learning algorithms and neural networks function is essential.
  • Proficiency in a programming language, with Python being the preferred choice, to implement AI models effectively.
  • Experience using AWS services such as S3, EC2, and SageMaker will be beneficial.
  • Hands-on experience with deep learning frameworks like TensorFlow or PyTorch is important for working with Generative AI models.
  • Familiarity with data processing techniques and ETL (Extract, Transform, Load) tasks is necessary for handling datasets in AI projects.
Course Objectives
  • Gain a foundational understanding of Generative AI and its applications.
  • Learn how to plan and execute Generative AI projects effectively.
  • Master the use of Amazon Bedrock for AI model development and deployment.
  • Develop skills in prompt engineering, including advanced techniques and bias mitigation.
  • Understand the components of Generative AI applications and how to secure them.
  • Explore Amazon Bedrock Foundation Models for inference and data protection.
  • Integrate AWS tools with LangChain to optimize Large Language Model (LLM) performance.
  • Apply architecture patterns for various Generative AI use cases, including text summarization, question answering, chatbots, and code generation.
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How We Help You Succeed

Vision

Vision

Goal

Goal

Skill-Building

Skill-Building

Mentoring

Mentoring

Direction

Direction

Support

Support

Success

Success

Career Transformation

Career Transformation

2 Million

Projected increase in roles related to Generative AI and AI-driven application

Up to 60% Innovation Boost

Organizations integrating Generative AI with AWS report

To tackle the skills shortage
85%

of Organizations: Plan to hire professionals skilled in AWS Generative AI tools, recognizing the critical importance of AI-driven solutions in maintaining a competitive edge.

75%

of Organizations: Committed to training staff on AWS Generative AI practices to enhance their expertise and effectively leverage AI technologies for business growth.

Demand across industries
Technology

Technology

Healthcare

Healthcare

Retail

Retail

Government

Government

Manufacturing

Manufacturing

Finance

Finance

Career Transformation
Career Transformation

Words Have Power

Success Speaks Volumes

Success Story

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Frequently Asked Questions

What is the Developing Generative AI Applications on AWS course?

The Developing Generative AI Applications on AWS course is designed to teach you how to build, deploy, and optimize AI-driven applications using Amazon Web Services (AWS).

Who should take the

This course is ideal for:

  • Data Scientists
  • Machine Learning Engineers
  • Software Developers
  • Innovation Teams in Enterprises
  • IT Professionals

What will I learn in the

In this course, you will learn the fundamentals of Generative AI, how to plan and execute AI projects, and how to use AWS tools like Amazon Bedrock for developing AI applications. You will gain skills in prompt engineering, model fine-tuning, and AI application architecture. Additionally, you will explore advanced topics such as integrating AWS with LangChain and applying architecture patterns for AI-driven solutions like text summarization, chatbots, and code generation.

Is there a certification exam for Developing Generative AI Applications on AWS?

There is no official certification exam specifically for Developing Generative AI Applications on AWS.

What are the prerequisites for enrolling in this course?

The prerequisites for enrolling in this course include:

  • A foundational grasp of how machine learning algorithms and neural networks function is essential.
  • Proficiency in a programming language, with Python being the preferred choice, to implement AI models effectively.
  • Experience using AWS services such as S3, EC2, and SageMaker will be beneficial.
  • Hands-on experience with deep learning frameworks like TensorFlow or PyTorch is important for working with Generative AI models.
  • Familiarity with data processing techniques and ETL (Extract, Transform, Load) tasks is necessary for handling datasets in AI projects.

How long does it take to complete the Developing Generative AI Applications on AWS course?

The training duration for the Developing Generative AI Applications on AWS course is 16 hours.

What career opportunities are available after completing this course?

After completing this course, you will be well prepared for roles such as AI Developer, Machine Learning Engineer, Data Scientist, AI Solutions Architect, and AWS Specialist.

Can I access the course materials after completing the course?

Yes, at InfosecTrain, we offer comprehensive post-training support. After completing the course, you'll have ongoing access to session recordings and course materials, ensuring you can revisit and reinforce your learning whenever needed.

Is there a refund policy if I’m not satisfied with the course?

For refund policies, we recommend checking the specific terms and conditions of the training, or you can directly contact InfosecTrain sales team at sales@infosectrain.com.

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