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Building AI with Bedrock Agent

Guy Ernest

Dive into the world of MLOps for generative AI with our comprehensive "Building AI with Bedrock Agent" course. This four-week program is designed to equip you with the skills and knowledge needed to develop enterprise-grade AI applications using AWS Bedrock and Python.

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Dive into the world of MLOps for generative AI with our comprehensive "Building AI with Bedrock Agent" course. This four-week program is designed to equip you with the skills and knowledge needed to develop enterprise-grade AI applications using AWS Bedrock and Python.

Key course highlights:

  • Gain a deep understanding of Large Language Models (LLMs) and their applications
  • Learn MLOps best practices for implementing generative AI in enterprise environments
  • Develop proficiency in AWS cloud infrastructure and Bedrock Agent technology
  • Master Infrastructure as Code (IaC) using AWS CDK
  • Explore data engineering and DataOps techniques for AI applications
  • Build and optimize Bedrock Agents with AWS Lambda and DevOps practices
  • Create user interfaces and operational dashboards for AI solutions

By the end of this course, you'll have the skills to build, deploy, and manage generative AI applications using AWS Bedrock, setting you up for success in the rapidly evolving field of AI development.

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What's inside

Learning objectives

  • Llm fundamentals and aws bedrock implementation
  • Infrastructure as code with aws cdk
  • Data engineering and pipeline optimization techniques
  • Bedrock agent development and lambda tool integration
  • User interface design for ai applications
  • Mlops best practices for enterprise ai solutions
  • Trustworthy ai principles and customization strategies
  • Operational dashboard creation and it considerations

Syllabus

Week 1: Introduction to MLOps for LLM
Fundamentals of LLMs, tokenization, embeddings, and attention mechanisms
Hands-on experience with AWS Bedrock console and model access
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Trustworthy AI principles and LLM customization
Week 2: IaC and DataOps
AWS CDK for infrastructure management
Data engineering practices and pipeline improvements
Handling messy data with AWS Glue and Lambda
Week 3: Building Bedrock Agent
Designing and implementing Lambda tools for Bedrock Agents
Creating and optimizing Bedrock Agents using CDK and management console
Week 4: Building Interfaces and Operations
Developing chat UI with FastAPI and HTMX
Deploying applications with AWS App Runner
Creating operational dashboards and conducting IT reviews

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches fundamentals of large language models and their tokenization, embeddings, and attention mechanisms
Examines MLOps best practices for enterprise-grade AI applications
Provides hands-on experience with AWS Bedrock console and model access
Develops proficiency in AWS cloud infrastructure and Bedrock Agent technology
Teaches learners to master Infrastructure as Code (IaC) using AWS CDK
Provides an overview of data engineering and data pipeline optimization techniques

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building AI with Bedrock Agent with these activities:
Review Python basics and best practices
Refresh your knowledge of Python basics and best practices to strengthen your foundation for generative AI development.
Browse courses on Python Basics
Show steps
  • Review core Python concepts such as data types, variables, and control flow.
  • Practice writing simple Python scripts to reinforce your understanding.
  • Study Python best practices for code readability, maintainability, and efficiency.
Complete coding exercises on AWS CDK
Sharpen your skills in AWS CDK by completing coding exercises, strengthening your ability to manage cloud infrastructure as code.
Browse courses on AWS CDK
Show steps
  • Find online coding challenges or practice platforms that offer exercises on AWS CDK.
  • Solve the exercises to reinforce your understanding of CDK syntax and concepts.
  • Review your solutions and identify areas for improvement.
Gather resources on Trustworthy AI principles and customization
Collect and organize resources on Trustworthy AI principles and customization to deepen your understanding of ethical and responsible AI practices.
Browse courses on Trustworthy AI
Show steps
  • Search for articles, whitepapers, and guidelines on Trustworthy AI principles.
  • Review and summarize the key takeaways from these resources.
  • Explore case studies and examples of how AI customization can improve fairness and bias mitigation.
Four other activities
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Show all seven activities
Explore AWS Lambda and DevOps practices
Enhance your understanding of AWS Lambda and DevOps by following guided tutorials, expanding your knowledge of serverless computing and cloud infrastructure.
Browse courses on AWS Lambda
Show steps
  • Enroll in online tutorials or courses on AWS Lambda and DevOps principles.
  • Work through hands-on exercises to gain practical experience with Lambda functions and DevOps tools.
  • Apply what you learn to build and deploy simple serverless applications.
Collaborate on a Bedrock Agent project with classmates
Engage with your classmates on a collaborative Bedrock Agent project, fostering teamwork and sharing knowledge.
Show steps
  • Form a team with classmates who share similar interests or expertise.
  • Choose a project that aligns with the course objectives and provides opportunities for learning.
  • Divide tasks and responsibilities among team members based on their strengths and interests.
  • Collaborate regularly to discuss progress, share ideas, and provide feedback.
  • Present your project findings and insights to the class.
Develop a user interface for your Bedrock Agent
Create a user interface for your Bedrock Agent to enhance its usability and make it accessible to a wider audience.
Browse courses on User Interface Design
Show steps
  • Plan the user interface design based on the needs of your specific application.
  • Choose appropriate UI frameworks and tools, such as FastAPI and HTMX.
  • Implement the user interface using the selected tools and wire it up to your Bedrock Agent.
  • Test and iterate on the user interface to improve its usability and responsiveness.
Contribute to open-source projects related to MLOps and AI
Make valuable contributions to open-source projects in the MLOps and AI domain, expanding your knowledge and giving back to the community.
Browse courses on Open Source
Show steps
  • Identify open-source projects related to MLOps and AI that align with your interests.
  • Review project documentation and code to understand the project's goals and functionalities.
  • Identify areas where you can contribute, such as bug fixing, feature implementation, or documentation updates.
  • Contribute your changes to the project by following the project's contribution guidelines.

Career center

Learners who complete Building AI with Bedrock Agent will develop knowledge and skills that may be useful to these careers:

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Course image
Effort
4 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructor
Guy Ernest
Session
July 12, 2024 - July 15, 2027
Language
English
Transcript
English
Good to know
Teaches fundamentals of large language models and their tokenization, embeddings, and attention mechanisms
Examines MLOps best practices for enterprise-grade AI applications
Provides hands-on experience with AWS Bedrock console and model access
Develops proficiency in AWS cloud infrastructure and Bedrock Agent technology
Teaches learners to master Infrastructure as Code (IaC) using AWS CDK
Provides an overview of data engineering and data pipeline optimization techniques
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