Tutor LogoTutor

AWS Bedrock for Generative AI in Consumer Tech

This comprehensive course is designed for engineers, data scientists, and architects at consumer tech enterprises to learn how to leverage Amazon Bedrock for building generative AI applications. Through hands-on labs and real-world examples, participants will explore foundational models, customization techniques, and the implementation of AI-driven solutions.

What You'll Learn

  • Understand the capabilities of AWS Bedrock and its foundational models
  • Set up and access AWS Bedrock for generative AI applications
  • Customize foundation models with enterprise data for improved performance
  • Implement Retrieval-Augmented Generation (RAG) for accurate responses
  • Build and deploy AI agents for automated customer support

AI Mentor Inspiration

Ada Bedrock

Ada Bedrock

Ada Bedrock is an AI tutor with expertise in generative AI and AWS services, dedicated to guiding learners through the complexities of building AI applications.

Detailed Schedule

Week 1

Introduction to AWS Bedrock and Foundation Models

Learn about Amazon Bedrock, its capabilities, and foundational models that enable generative AI applications.

  • Topics:
    • Overview of AWS Bedrock
    • Understanding foundation models
  • Live Session Duration: 60 minutes
  • Homework Duration: 120 minutes
  • Discussion Points:
    • What potential applications do you see for generative AI in your industry?
    • How do foundation models differ from traditional AI models?
    • What ethical considerations should we keep in mind when using generative AI?
  • Reading Assignments:
    • AWS Bedrock Documentation: Overview and Features
    • Article: 'Generative AI: Opportunities and Challenges'
    • Case Study: 'Generative AI in Consumer Tech'
  • Video Assignments:
    • Watch: 'Introduction to AWS Bedrock'
    • Tutorial: 'Exploring Foundation Models'

Week 2

Setting Up and Accessing AWS Bedrock

Hands-on experience with setting up your AWS environment for Bedrock and accessing foundational models.

  • Topics:
    • Creating an AWS account
    • Configuring permissions and roles
  • Live Session Duration: 60 minutes
  • Homework Duration: 150 minutes
  • Discussion Points:
    • What challenges did you face while setting up AWS?
    • How can we ensure secure access to AWS Bedrock?
    • What are the best practices for managing AWS permissions?
  • Reading Assignments:
    • AWS IAM Documentation: Best Practices
    • Article: 'Getting Started with AWS Bedrock'
    • Tutorial: 'Configuring Your AWS Environment'
  • Video Assignments:
    • Watch: 'Setting Up AWS Bedrock'
    • Tutorial: 'Navigating the Bedrock Console'

Week 3

Model Selection and Experimentation in AWS Bedrock

Learn how to choose the right foundation model for specific tasks and experiment with model outputs in the Bedrock Playground.

  • Topics:
    • Choosing the right model
    • Hands-on model experimentation
  • Live Session Duration: 90 minutes
  • Homework Duration: 180 minutes
  • Discussion Points:
    • What criteria do you use to select a model for your application?
    • How do different models perform on similar tasks?
    • What insights can we gain from comparing model outputs?
  • Reading Assignments:
    • AWS Bedrock Model Documentation
    • Research Paper: 'Evaluating Foundation Models'
    • Case Study: 'Model Selection in Practice'
  • Video Assignments:
    • Watch: 'Experimenting with AWS Bedrock Models'
    • Tutorial: 'Using the Bedrock Playground'

Week 4

Customizing Foundation Models with Enterprise Data

Explore how to fine-tune foundation models using your own enterprise data to enhance their effectiveness.

  • Topics:
    • Fine-tuning process
    • Preparing training data
  • Live Session Duration: 90 minutes
  • Homework Duration: 240 minutes
  • Discussion Points:
    • What types of data are best for fine-tuning?
    • How do we avoid overfitting when customizing models?
    • What are the benefits of using enterprise data?
  • Reading Assignments:
    • AWS Bedrock Customization Guide
    • Article: 'Fine-Tuning AI Models'
    • Research Paper: 'The Impact of Custom Data on AI Performance'
  • Video Assignments:
    • Watch: 'Fine-Tuning Models in AWS Bedrock'
    • Tutorial: 'Preparing Data for Customization'

Week 5

Implementing Retrieval-Augmented Generation (RAG)

Learn how to enhance model outputs by integrating external data sources using RAG techniques.

  • Topics:
    • Understanding RAG
    • Building a knowledge base
  • Live Session Duration: 90 minutes
  • Homework Duration: 180 minutes
  • Discussion Points:
    • How does RAG improve the accuracy of AI responses?
    • What types of external data sources can be integrated?
    • What challenges might arise when implementing RAG?
  • Reading Assignments:
    • AWS Bedrock RAG Documentation
    • Article: 'Enhancing AI with RAG'
    • Case Study: 'RAG in Action'
  • Video Assignments:
    • Watch: 'Implementing RAG with AWS Bedrock'
    • Tutorial: 'Creating a Knowledge Base'

Week 6

Building and Deploying AI Agents

Learn to create autonomous AI agents that can handle multi-step tasks and integrate with APIs.

  • Topics:
    • Defining AI agents
    • Configuring agent actions
  • Live Session Duration: 120 minutes
  • Homework Duration: 240 minutes
  • Discussion Points:
    • What are the key components of an AI agent?
    • How do agents differ from standard chatbots?
    • What are the best practices for agent deployment?
  • Reading Assignments:
    • AWS Bedrock Agent Documentation
    • Article: 'Building Intelligent Agents'
    • Research Paper: 'AI Agents in Customer Support'
  • Video Assignments:
    • Watch: 'Creating AI Agents in AWS Bedrock'
    • Tutorial: 'Testing and Deploying Agents'

Week 7

Security, Compliance, and Responsible AI Practices

Understand the importance of security and compliance when implementing AI solutions, and learn responsible AI practices.

  • Topics:
    • Data security measures
    • Ethical AI considerations
  • Live Session Duration: 90 minutes
  • Homework Duration: 180 minutes
  • Discussion Points:
    • What are the main security concerns when using AI?
    • How can we ensure compliance with data protection regulations?
    • What ethical considerations should guide AI development?
  • Reading Assignments:
    • AWS Security Best Practices
    • Article: 'Responsible AI Guidelines'
    • Research Paper: 'Ethics in AI Development'
  • Video Assignments:
    • Watch: 'Security Practices in AWS Bedrock'
    • Tutorial: 'Implementing Responsible AI'

Week 8

Optimizing Inference Latency and Cost Management

Learn techniques to optimize the performance and cost of AI applications built with AWS Bedrock.

  • Topics:
    • Latency optimization techniques
    • Cost management strategies
  • Live Session Duration: 90 minutes
  • Homework Duration: 180 minutes
  • Discussion Points:
    • What strategies can reduce inference latency?
    • How can we effectively manage costs associated with AI models?
    • What role does monitoring play in cost management?
  • Reading Assignments:
    • AWS Cost Management Documentation
    • Article: 'Optimizing AI Costs'
    • Research Paper: 'Performance Tuning for AI Models'
  • Video Assignments:
    • Watch: 'Cost Management in AWS Bedrock'
    • Tutorial: 'Optimizing AI Performance'

Week 9

Case Study: Implementing Generative AI for an Online Travel Platform

Analyze a real-world case study of a travel platform using AWS Bedrock to enhance its services with generative AI.

  • Topics:
    • Case study analysis
    • Identifying key success factors
  • Live Session Duration: 90 minutes
  • Homework Duration: 180 minutes
  • Discussion Points:
    • What were the key challenges faced in the case study?
    • How did the platform leverage AWS Bedrock effectively?
    • What lessons can be applied to future AI projects?
  • Reading Assignments:
    • Case Study: 'Generative AI in Travel'
    • Article: 'Lessons Learned from AI Implementations'
    • Research Paper: 'AI in Consumer Industries'
  • Video Assignments:
    • Watch: 'Case Study Overview'
    • Tutorial: 'Key Takeaways from the Case Study'

Week 10

Capstone Project: Building a Generative AI-Powered Travel Assistant

Apply all the knowledge gained throughout the course to build a fully functional generative AI travel assistant.

  • Topics:
    • Project planning and design
    • Implementation and testing
  • Live Session Duration: 120 minutes
  • Homework Duration: 240 minutes
  • Discussion Points:
    • What features should be prioritized in the travel assistant?
    • How can we ensure the assistant meets user needs?
    • What testing methods can validate the assistant's performance?
  • Reading Assignments:
    • Project Guidelines and Requirements
    • Article: 'Best Practices for AI Project Development'
    • Research Paper: 'Evaluating AI Solutions'
  • Video Assignments:
    • Watch: 'Capstone Project Overview'
    • Tutorial: 'Building Your Travel Assistant'

Student Experiences

Word Cloud

Weekly group discussions to share insightsCollaborative projects to enhance learningPeer feedback on assignments and projects

Learning with Cohorts and AI

Learn Better with Cohorts

Collaborative learning fosters diverse perspectives, encourages knowledge sharing, and simulates real-world team dynamics, enhancing overall understanding.

AI-Powered Learning

AI support provides personalized guidance, instant feedback, and tailored explanations, ensuring that learners receive assistance exactly when they need it.

Frequently Asked Questions

What is the main focus of the course?

The course focuses on leveraging AWS Bedrock to build generative AI applications in consumer tech, including customization and implementation of AI-driven solutions.

What are the prerequisites?

Participants should have a basic understanding of cloud computing and AI concepts. Familiarity with AWS services is beneficial but not mandatory.

How is the course structured?

The course includes weekly live sessions, hands-on labs, reading assignments, and a capstone project that integrates all learned skills.

What kind of support is provided?

Support includes live instructor-led sessions, personalized AI tutor assistance, and peer collaboration opportunities.

Is there a certificate upon completion?

Yes, participants receive a certificate of completion after successfully finishing the course and the capstone project.

Course Details

  • 10 weeks
  • Cohort-based learning
  • Start anytime

Join our community of learners and kickstart your journey to success.

10 weeksFREE