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Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda

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Based on 50 ratings
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Soumen Kumar Mondal

This course is ideal for students, data scientists, AI/ML engineers, developers, and product managers who want to master Generative AI using AWS Bedrock and Google Vertex AI. No prior Python experience is required, making it accessible to beginners eager to dive into the world of AI without the steep learning curve.

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This course is ideal for students, data scientists, AI/ML engineers, developers, and product managers who want to master Generative AI using AWS Bedrock and Google Vertex AI. No prior Python experience is required, making it accessible to beginners eager to dive into the world of AI without the steep learning curve.

We’ll cover the essentials of Generative AI and cloud computing before delving into hands-on projects using AWS Bedrock services like S3, Lambda, and API Gateway. You’ll build applications with knowledge base creation, RAG (Retrieval-Augmented Generation), and guardrail setups to ensure safe, reliable AI outputs.

In addition, you'll explore Google Vertex AI, where we’ll cover Agentic AI for dynamic, real-time decision-making and Vertex AI RAG to create intelligent AI systems. You’ll also integrate APIs and cloud functions to enhance your applications further.We'll cover AWS AI offerings like Amazon Q, SageMaker AI and Google cloud AI offerings.

With comprehensive hands-on practice and clear explanations, this course ensures that you gain practical skills in Generative AI and cloud AI services. By the end, you’ll be equipped to build scalable, AI-powered applications, making it perfect for advancing your career in the ever-evolving AI field. The course will get updated with new content regularly.

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What's inside

Learning objectives

  • Fundamentals of generative ai – understand the core concepts of generative ai, including prompt engineering, cloud computing, and how ai models work.
  • Aws bedrock – learn how to set up aws bedrock, explore the console, and use services like s3, lambda, and api gateway to build ai-powered application
  • Knowledge base and retrieval-augmented generation (rag) – build applications that use rag to interact with custom data stored in s3 using aws bedrock.
  • Agent creation and guardrails – create powerful agents with guardrails to ensure safe and reliable interactions with ai models.
  • Google vertex ai – explore google’s vertex ai offerings, learn how to set up vertex ai studio, and implement rag and agentic ai use cases.
  • Hands-on projects – implement real-world use cases, including building ai knowledge base applications, travel agent apps, and integrating external apis and clou
  • Cloud integration – learn how to effectively integrate generative ai with cloud services for scalability, security, and storage.

Syllabus

Introduction

In this introduction section, we'll explore the exciting world of Generative AI and how it merges with powerful cloud platforms like AWS Bedrock and Google Vertex AI. Get ready to dive into hands-on projects that will equip you with the skills to build AI-driven applications!

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In this lecture, we'll explore the fundamentals of Cloud Computing and how it powers Generative AI applications. Learn how cloud platforms like AWS and Google Cloud provide the scalability, security, and tools needed to enhance AI solutions.

In this lecture, we'll cover the fundamentals of Generative AI. This beginner-friendly introduction will set the stage for your journey into building powerful AI applications using cloud technologies.

In this lecture, we'll introduce you to the basics of Prompt Engineering, teaching you how to craft effective prompts to get the best responses from AI models.

Will do hands on of prompt engineering on ChatGPT

Basic idea and overview of agentic AI. Difference between non agentic and agentic AI flow.

Few details of agentic AI.

In this lecture, we’ll explore the AWS Generative AI Services- Amazon Q, Amazon Bedrock, Sagemaker AI, AI infra.

In this lecture, we’ll dive into the concepts and features of AWS Bedrock, Amazon’s fully managed service for building and scaling Generative AI applications. You’ll learn about its powerful integration with pre-trained models, custom model support, and how it enables seamless interaction with AI technologies like LLMs for various use cases.

In this lecture, we'll walk you through the AWS Bedrock account setup, guiding you step-by-step on how to create and configure your AWS account for using Bedrock. Learn how to navigate the AWS Console and get everything ready to start building your Generative AI applications.

In this lecture, we'll provide an overview of the AWS Bedrock console, showing you how to navigate its interface and access key features. You’ll learn how to manage models, monitor usage, and explore the tools available for building and deploying your Generative AI applications.

In this lecture, we’ll guide you through accessing LLMs (Large Language Models) within AWS Bedrock and demonstrate how to use the Bedrock Playground for experimentation. You’ll learn how to interact with pre-trained models, fine-tune them, and explore their capabilities in a hands-on, user-friendly environment.

Use case architecture including Bedrock, S3, API gateway, Lambda

Lambda code to integrate with Boto3 and S3.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Covers AWS Bedrock and Google Vertex AI, which are essential platforms for developing and deploying generative AI applications in the cloud
Teaches how to integrate generative AI with cloud services, which is crucial for building scalable and secure AI-powered applications
Requires no prior Python experience, making it accessible for newcomers to learn the fundamentals of generative AI and cloud computing
Explores Agentic AI for dynamic, real-time decision-making, which is a cutting-edge approach to AI development
Focuses on building applications with knowledge base creation, RAG, and guardrail setups, which are critical for ensuring safe and reliable AI outputs
Requires learners to set up accounts with AWS and Google Cloud, which may involve costs depending on usage beyond free tiers

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Reviews summary

Agentic ai & rag on aws and google cloud

Note: No reviews were provided for analysis. This summary and notes are based on the course description and syllabus. Based on its description, this course appears designed for professionals and students aiming to master Generative AI on major cloud platforms. It promises hands-on experience building AI-driven applications using AWS Bedrock and Google Vertex AI, covering topics like RAG, Agentic AI, knowledge bases, and guardrails. The course outlines practical projects and claims no prior Python experience is required, suggesting accessibility for beginners in AI, although familiarity with cloud concepts might be beneficial.
Explores fundamentals of Generative & Agentic AI.
"Understand the core concepts of Generative AI and cloud computing."
"Learn about prompt engineering and agentic AI flows."
Covers Generative AI on AWS & Google.
"This course teaches Generative AI using both AWS Bedrock and Google Vertex AI."
"Learn to build AI applications on two major cloud platforms."
Analysis based only on course description.
"No student reviews were available to analyze the course's effectiveness."
"The sentiment and notes are derived solely from the course's self-description."
No prior Python experience needed.
"The course states that no prior Python experience is required."
"It is designed to be accessible for beginners in the AI field."
Includes hands-on use cases and implementation.
"The course includes hands-on projects like building knowledge bases and AI assistants."
"Implement real-world use cases for AI-powered applications."

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 Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda with these activities:
Review Cloud Computing Fundamentals
Solidify your understanding of cloud computing concepts to better grasp the infrastructure supporting Generative AI applications in AWS Bedrock and Google Vertex AI.
Browse courses on Cloud Computing
Show steps
  • Review the basics of cloud computing models (IaaS, PaaS, SaaS).
  • Understand the differences between AWS and Google Cloud.
  • Familiarize yourself with key cloud services like compute, storage, and networking.
Brush Up on Python Basics
Practice basic Python syntax and data structures to prepare for coding examples and hands-on projects using Boto3 and Streamlit.
Browse courses on Python
Show steps
  • Review Python syntax, data types, and control flow.
  • Practice writing simple Python scripts.
  • Familiarize yourself with common Python libraries.
Read 'Generative AI with Python and TensorFlow 2'
Gain a deeper understanding of generative AI models and techniques to enhance your ability to work with AWS Bedrock and Google Vertex AI.
View Generative AI with Python and TensorFlow 2:... on Amazon
Show steps
  • Read the chapters related to GANs and VAEs.
  • Experiment with the code examples provided in the book.
  • Relate the concepts to the models used in AWS Bedrock and Google Vertex AI.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Simple Chatbot with AWS Lex
Practice building a chatbot using AWS Lex to understand conversational AI concepts and prepare for more advanced agentic AI applications.
Show steps
  • Design the chatbot's conversation flow and intents.
  • Implement the chatbot using AWS Lex.
  • Test and refine the chatbot's responses.
Write a Blog Post on RAG Applications
Solidify your understanding of Retrieval-Augmented Generation (RAG) by explaining the concept and its applications in a blog post.
Show steps
  • Research RAG and its use cases.
  • Outline the key points for your blog post.
  • Write and publish the blog post.
Create a Presentation on Agentic AI
Deepen your knowledge of Agentic AI by creating a presentation that explains its concepts, benefits, and use cases.
Show steps
  • Research Agentic AI and its applications.
  • Design the presentation slides with clear explanations and visuals.
  • Practice delivering the presentation.
Read 'LangChain in Action'
Explore LangChain as an alternative framework for building LLM-powered applications and compare its features with AWS Bedrock and Google Vertex AI.
View Melania on Amazon
Show steps
  • Read the chapters related to prompt engineering and agent creation.
  • Experiment with the code examples provided in the book.
  • Compare LangChain's approach with the methods taught in the course.

Career center

Learners who complete Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda will develop knowledge and skills that may be useful to these careers:
Generative AI Specialist
A Generative AI Specialist focuses specifically on creating and implementing Generative AI models. This course allows one to master Generative AI using AWS Bedrock and Google Vertex AI. The course covers the essentials of Generative AI and cloud computing, with hands-on projects that use AWS Bedrock services like S3 and Lambda. You will build applications with knowledge base creation, RAG (Retrieval-Augmented Generation), and guardrail setups. In addition, you'll explore Google Vertex AI, where we’ll cover Agentic AI for dynamic, real-time decision-making. This course helps any Generative AI Specialist.
See salaries and explore the career path for Generative AI Specialist
AI Engineer
An AI Engineer develops, tests, and deploys AI models and applications. This course helps build a strong foundation in Generative AI, particularly with AWS Bedrock and Google Vertex AI, which are increasingly important in the field. This course provides hands-on experience with essential tools like S3, Lambda, and API Gateway on AWS, and Agentic AI on Google Vertex AI. The practical skills gained would allow an AI Engineer to design and implement AI solutions effectively. The course's emphasis on RAG (Retrieval-Augmented Generation) and guardrail setups is directly applicable to creating reliable and safe AI outputs.
See salaries and explore the career path for AI Engineer
Machine Learning Engineer
A Machine Learning Engineer focuses on building and deploying machine learning models into production systems. This course provides hands-on experience with AWS Bedrock and Google Vertex AI, it allows one to master Generative AI. The course covers key concepts such as prompt engineering, cloud computing, and AI model implementation, all of which are crucial for success as a Machine Learning Engineer. The practical projects using S3, Lambda, and API Gateway is highly relevant, as are the sections on RAG (Retrieval-Augmented Generation) and Agentic AI. The use of Amazon Q and SageMaker AI are also invaluable.
See salaries and explore the career path for Machine Learning Engineer
AI Developer
An AI Developer builds and implements AI-powered applications and solutions. This course helps build a strong foundation in Generative AI using AWS Bedrock and Google Vertex AI. The hands-on projects involve creating applications with knowledge base creation and RAG (Retrieval-Augmented Generation), which are essential skills for an AI Developer. The course also covers integrating APIs and cloud functions, enhancing the ability to build comprehensive AI solutions. The material on Amazon Q and SageMaker AI may be useful in expanding one's repertoire.
See salaries and explore the career path for AI Developer
Cloud Solutions Architect
A Cloud Solutions Architect designs and implements cloud-based solutions for organizations. This course helps build a strong understanding of cloud platforms like AWS and Google Cloud, particularly in the context of Generative AI. The course covers AWS Bedrock services such as S3, Lambda, and API Gateway, as well as Google Vertex AI, offering valuable insights into building scalable, secure, and efficient cloud architectures. The hands-on projects involving RAG (Retrieval-Augmented Generation) and Agentic AI are relevant for designing advanced AI applications. The coverage of Amazon Q and SageMaker AI can help in architecting comprehensive AI solutions.
See salaries and explore the career path for Cloud Solutions Architect
Cloud Engineer
A Cloud Engineer implements, manages, and supports cloud computing environments. This course provides practical skills in using AWS Bedrock and Google Vertex AI for building and deploying AI applications. The course covers essential cloud services like S3, Lambda, and API Gateway, along with hands-on projects that replicate real-world scenarios. The knowledge of cloud integration, scalability, and security enhances the skills of a Cloud Engineer. This course is useful in particular as the cloud AI services landscape is constantly evolving.
See salaries and explore the career path for Cloud Engineer
Data Engineer
A Data Engineer builds and maintains the infrastructure required for data storage and processing. This course provides hands-on experience with cloud services like AWS S3, Lambda, and API Gateway, which are crucial for building scalable data pipelines for AI applications. The sections on integrating Generative AI with cloud services for scalability, security, and storage are particularly relevant. The knowledge gained would help a Data Engineer design and implement efficient data solutions to support AI initiatives. The integration of APIs and cloud functions can help streamline data workflows.
See salaries and explore the career path for Data Engineer
Solutions Architect
A Solutions Architect designs and implements technical solutions to address business problems. This course helps in understanding how to integrate Generative AI capabilities into solution architectures. The hands-on experience with AWS Bedrock and Google Vertex AI, including services like S3, Lambda, and API Gateway, provides practical skills. The knowledge of RAG (Retrieval-Augmented Generation) and Agentic AI allows the architect to design intelligent systems. The cloud integration strategies covered in this course would be invaluable in creating scalable and efficient solutions.
See salaries and explore the career path for Solutions Architect
AI Ethics Officer
An AI Ethics Officer ensures that AI systems are developed and used responsibly and ethically. This course is useful by providing a foundational understanding of Generative AI technologies and their applications on platforms like AWS Bedrock and Google Vertex AI. The sections on guardrail setups, which ensure safe and reliable AI outputs, is valuable for an AI Ethics Officer. The exposure to various AI services and their implementations might also be useful in identifying potential ethical concerns and developing mitigation strategies.
See salaries and explore the career path for AI Ethics Officer
AI Product Manager
An AI Product Manager defines the strategy and roadmap for AI-powered products. This course helps build a foundational understanding of Generative AI technologies on AWS Bedrock and Google Vertex AI. The exploration of cloud computing, prompt engineering, and AI model implementation might be valuable. The course's emphasis on RAG (Retrieval-Augmented Generation) and guardrail setups provides insights into the practical considerations of building safe and reliable AI products. The hands-on projects will allow one to understand the technical aspects, thereby enhancing product development decisions.
See salaries and explore the career path for AI Product Manager
AI Consultant
An AI Consultant advises organizations on how to leverage AI technologies to achieve their business goals. This course may be useful by providing a comprehensive understanding of Generative AI and its applications. The sections on AWS Bedrock and Google Vertex AI, along with hands-on projects, equip you with practical knowledge. The coverage of RAG (Retrieval-Augmented Generation) and Agentic AI gives insights into cutting-edge AI strategies. Learning about Amazon Q and SageMaker AI might be useful in providing well-rounded advice to clients.
See salaries and explore the career path for AI Consultant
Data Scientist
A Data Scientist analyzes data, builds models, and extracts insights to inform business decisions. This course may be useful by providing hands-on experience with Generative AI tools on AWS Bedrock and Google Vertex AI. It allows one to learn prompt engineering, cloud computing, and AI model implementation. The course’s focus on RAG (Retrieval-Augmented Generation) is relevant for developing AI-driven data analysis tools. The hands-on projects, including building AI knowledge base applications, could enhance analytical capabilities. Specifically, the integration of APIs and cloud functions might be useful for streamlining data workflows.
See salaries and explore the career path for Data Scientist
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful by providing the skills to integrate Generative AI into existing software systems. The hands-on experience with AWS Bedrock and Google Vertex AI, including services like S3, Lambda, and API Gateway, is valuable for building cloud-native AI applications. The sections on RAG (Retrieval-Augmented Generation) and integrating external APIs and cloud functions into applications might be useful. The exploration of Amazon Q and SageMaker AI is a useful addition to a Software Engineer's skillset.
See salaries and explore the career path for Software Engineer
Machine Learning Researcher
A Machine Learning Researcher explores and develops new algorithms and techniques in machine learning. Machine Learning Researchers typically have advanced degrees (master's or phd for example). This course may be useful by providing hands-on experience with AWS Bedrock and Google Vertex AI, allowing one to experiment with Generative AI models. The focus on prompt engineering, cloud computing, and AI model implementation could translate into a better understanding of applied ML. The sections on RAG (Retrieval-Augmented Generation) and Agentic AI may be useful in developing innovative solutions.
See salaries and explore the career path for Machine Learning Researcher
AI Research Scientist
An AI Research Scientist conducts research to advance the field of artificial intelligence. AI Research Scientists typically have advanced degrees (master's or phd for example). This course may be useful as an introduction to Generative AI tools and platforms, particularly AWS Bedrock and Google Vertex AI. The course covers the fundamentals of Generative AI, cloud computing, and AI model implementation, which could be valuable for understanding current industry practices. The sections on RAG (Retrieval-Augmented Generation) and Agentic AI may be useful.
See salaries and explore the career path for AI Research Scientist

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda.
Cover image
Generative AI with Python and TensorFlow 2
Save
Provides a comprehensive guide to generative AI models using Python and TensorFlow 2. It covers various techniques, including GANs, VAEs, and transformers, offering practical examples and code implementations. While the course focuses on AWS Bedrock and Google Vertex AI, this book offers a broader understanding of the underlying models. It is valuable as additional reading to deepen your knowledge of generative AI.
Generative AI with Python and TensorFlow 2: Create...
Paperback
$$

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Course image
Rating
4.5 Filled star Filled star Filled star Filled star Empty star
Effort
5.5 total hours
Via
Udemy
Instructor
Soumen Kumar Mondal
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Covers AWS Bedrock and Google Vertex AI, which are essential platforms for developing and deploying generative AI applications in the cloud
Teaches how to integrate generative AI with cloud services, which is crucial for building scalable and secure AI-powered applications
Requires no prior Python experience, making it accessible for newcomers to learn the fundamentals of generative AI and cloud computing
Explores Agentic AI for dynamic, real-time decision-making, which is a cutting-edge approach to AI development
Focuses on building applications with knowledge base creation, RAG, and guardrail setups, which are critical for ensuring safe and reliable AI outputs
Requires learners to set up accounts with AWS and Google Cloud, which may involve costs depending on usage beyond free tiers
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