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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.

Key course highlights:

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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.

Enroll now

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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Traffic lights

Read about what's good
what should give you pause
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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Reviews summary

Practical aws bedrock agent building

According to students, this course offers a highly practical and hands-on approach to building generative AI applications using AWS Bedrock Agent. Learners frequently praise the program's focus on MLOps best practices and leveraging AWS CDK for Infrastructure as Code (IaC), finding the content current and relevant for enterprise environments. While many highlight the clarity of instructor explanations and the value of practical projects, some note that the pace can be fast and it benefits significantly from prior AWS and Python expertise. Occasional minor issues with code examples are also mentioned.
Instructor provides clear explanations for complex technical topics.
"The instructor clearly explains complex concepts."
"The instructor's explanations were thorough."
"The instructor did a great job explaining complex concepts clearly."
"The demos were clear and concise."
Highly relevant for professionals implementing AI in enterprise settings.
"I appreciated the practical approach to MLOps best practices and AWS CDK for IaC."
"Highly practical and current. The focus on enterprise scenarios and trustworthy AI made it stand out."
"The best course I've taken on generative AI yet! The focus on enterprise-grade solutions and MLOps is precisely what I needed."
"My team can immediately apply what I learned from this course."
Course excels with practical labs and projects for real-world application.
"The hands-on labs with AWS Bedrock Agent setup and Lambda tool integration were incredibly valuable."
"Building a functional AI application from scratch with Bedrock was an eye-opener."
"The practical projects made understanding Bedrock Agent and Lambda tools much easier. The hands-on activities were key to learning."
"Excellent hands-on course for AWS Bedrock. The final project helped consolidate everything."
Occasional minor issues with code examples or dependencies.
"I found some of the code examples occasionally had minor issues or dependencies that were not immediately clear, requiring some debugging on my part."
"I found some of the CDK examples could be a bit more robust, but it's a minor point."
Requires prior AWS and Python expertise; pace can be challenging for some.
"Found this course quite challenging. It assumes a high level of prior AWS and Python expertise that wasn't clearly stated as a strict prerequisite."
"Sometimes, the pace felt a bit rushed, especially in the data engineering sections with Glue, assuming prior AWS Glue knowledge."
"I struggled with the pace and some of the coding examples."
"Some parts felt a bit superficial for advanced users. While Bedrock Agent concepts were covered, I wished for more in-depth exploration."

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
Expand to see all activities and additional details
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
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
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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