Name: Amazon Bedrock Masterclass: The Ultimate Generative AI Guide
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Creado porRenaldi Gondosubroto

Última actualización: 2/2025

Inglés

Lo que aprenderás

Acquire a comprehensive understanding of Amazon Bedrock’s architecture and its integration with AWS services to deploy generative AI applications.
Gain proficiency in utilizing the Amazon Bedrock API to access and interact with foundation models from various AI companies for generative tasks.
Master the techniques of prompt engineering to effectively communicate with language models and obtain desired responses for various AI applications.
Develop skills to customize, build, and scale generative AI applications using Amazon Bedrock, ensuring privacy and without managing infrastructure.

Contenido del curso

7 secciones • 41 clases • 4 h 3 m de duración total

Introduction to the course4:39
Kickstart your learning journey with this introductory lecture to the course. This session will provide an overview of the course structure, objectives, and the key topics that will be covered. It will also introduce Amazon Bedrock and its significance in building generative AI applications. Students will get a glimpse of the practical skills they will acquire and the innovative projects they will be able to create by the end of this course.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the structure and objectives of the course.
Familiarize themselves with the key topics and technologies that will be covered.
Grasp the significance of Amazon Bedrock in the realm of generative AI.
Envision the practical skills they will acquire and the types of projects they will be able to create by the end of the course.
Feel motivated and prepared to engage fully in the learning journey ahead.
Setting up your AWS account4:06
In this introductory lecture, students will be guided through the process of setting up their AWS (Amazon Web Services) accounts. This setup is the gateway to accessing a plethora of cloud services provided by Amazon, including Amazon Bedrock which will be covered in the subsequent lecture.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Create and configure their AWS accounts securely.
Navigate through the AWS Management Console.
What is Amazon Bedrock?4:49
In this lecture, students will be introduced to Amazon Bedrock, a platform designed to enable the creation of generative AI applications. The lecture will cover the core concepts, features, and the ecosystem of Amazon Bedrock, providing a solid foundation for the more technical aspects that follow.

Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the core concepts and features of Amazon Bedrock.
Identify the key components of the Amazon Bedrock ecosystem.
Recognize the potential of generative AI applications facilitated by Amazon Bedrock.
Explore the interface and services integrated with Amazon Bedrock.
Parameters and Considerations10:14
This lecture delves into the parameters within Amazon Bedrock that control and fine-tune the behavior of foundation models. Students will learn about important parameters like maxTokenCount, temperature, and topP, and how they influence the generated outputs.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand and explain the significance of various Bedrock parameters.
Configure Bedrock parameters to control the behavior of foundation models.
Experiment with different parameter settings to observe their impact on model outputs.
Utilize Bedrock parameters to optimize the performance of generative AI applications.
Crafting Your Initial Prompt7:32
Crafting an effective initial prompt is crucial for obtaining desired outputs from generative models. This lecture will provide guidelines and best practices for crafting prompts that communicate the task clearly to the model, alongside demonstrations on how to iteratively refine prompts to achieve better results.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the importance of crafting an effective initial prompt.
Apply best practices to craft and refine prompts for generative models.
Evaluate the effectiveness of different prompts based on the quality of the generated outputs.
Iterate on their initial prompts to improve the performance of their generative AI applications.
End of Section 1 Quiz

Introduction to Using the Amazon Bedrock Playground4:56
Dive into the Amazon Bedrock Playground on AWS Console to explore the interactive environment it offers. This lecture will guide students through the interface, functionalities, and how to run basic generative models within the playground.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Navigate the Amazon Bedrock Playground on AWS Console.
Execute basic tasks using generative models in the playground.
Understand the interactive environment and its capabilities.
Identify where to find resources within the playground for self-guided learning.
Using the Chat and Text Playground4:14
Explore the specific features of the Chat and Text Playground within Amazon Bedrock. This lecture emphasizes how to input prompts, view generated responses, and understand the models' workings to a preliminary extent.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Input prompts and view generated responses in the Chat and Text Playground.
Understand basic workings of the models within the playground.
Utilize the playground for experimenting with text generation and conversation simulation.
Using the Image Generation Playground1:38
Unveil the creative power of Amazon Bedrock with the Image Generation Playground. Students will learn to generate images from textual descriptions and understand the underlying generative models' basics.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Generate images from textual descriptions using the Image Generation Playground.
Understand the basic principles behind image generation models within Bedrock.
Experiment with different textual descriptions to generate varied imagery.
A Note for Getting through Neutrality4:17
Delve into the nuances of Bedrock’s LLM (Large Language Models) stance of neutrality. This lecture explores how to engineer prompts to guide models towards more opinionated responses while understanding the ethical considerations involved.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand Bedrock's LLM stance of neutrality and the rationale behind it.
Engineer prompts to elicit more opinionated responses from models.
Recognize the ethical considerations and implications of guiding model responses.
Using Examples from Amazon Bedrock0:51
Learn from real-world examples provided by AWS to understand how to utilize Amazon Bedrock effectively. This lecture showcases how to replicate and learn from these examples to build robust generative AI applications.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Replicate and understand examples provided by AWS for Amazon Bedrock.
Apply learnings from examples to their projects.
Recognize common patterns and best practices in utilizing Amazon Bedrock for various tasks.
Fine Tuning Foundational Models5:15
Delve into the advanced topic of fine-tuning foundational models within Amazon Bedrock to better suit specific tasks. This lecture provides a practical guide on how to fine-tune models while understanding the underlying principles.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the principles behind fine-tuning foundational models.
Apply fine-tuning techniques to improve model performance on specific tasks.
Evaluate the effectiveness of fine-tuning and iterate to achieve desired outcomes.
Further References for Using Amazon Bedrock3:21
This lecture provides a curated list of resources and references for students to delve deeper into using Amazon Bedrock. It aims to foster self-guided learning and exploration beyond the course content.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Identify resources for further learning and exploration of Amazon Bedrock.
Engage in self-guided learning to enhance their understanding and skills.
Connect with communities and forums for collaborative learning and problem-solving.
End of Section 2 Quiz

Foundational Models on Amazon Bedrock6:11
Explore the diverse range of foundational models available on Amazon Bedrock. This lecture covers how these models are structured, their capabilities, and how they can be utilized to build generative AI applications.
Note: As of recent, Amazon Bedrock has also added Llama, Mistral, and Claude 3 to the family of foundational models.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the variety and capabilities of foundational models on Amazon Bedrock.
Recognize the structure and functioning of these models.
Utilize foundational models to build various generative AI applications.
Note: To set your aws_access_key_id and aws_secret_access_key on Windows, you will have to open Settings -> System -> About -> Advanced System Settings -> Environment Variables. Under User variables for your username, click on New... and create two different variables; one for aws_access_key_id and aws_secret_access_key. Otherwise, you can also directly hardcode your aws_access_key_id and aws_secret_access_key in the notebook such as aws_access_key_id = "<YOUR_ACCESS_KEY>", though this is strictly not recommended.
Understanding Why we Use the API1:23
Delve into the importance and benefits of using the Amazon Bedrock API. This lecture elucidates how the API facilitates seamless interaction with Bedrock services and the value it brings to developing and deploying generative AI applications.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the benefits of using the Amazon Bedrock API.
Recognize how the API facilitates interaction with Bedrock services.
Appreciate the value the API brings to the development and deployment of generative AI applications.
Installing Python and Jupyter Notebook5:57
This lecture guides students through the installation of Python and Jupyter Notebook, setting the stage for hands-on programming and experimentation with Amazon Bedrock in the subsequent lectures.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Install Python and Jupyter Notebook on their machines.
Understand the basics of using Jupyter Notebook for interactive programming.
Prepare their development environment for the hands-on exercises in the following lectures.
Configuring Environmental Variables to be called Locally from Jupyter Notebook2:05
Performing Text Generation using the Bedrock API15:58
Engage in hands-on exercises to perform text generation using the Amazon Bedrock API. This lecture provides a practical guide on how to interact with the API to generate text and understand the results.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Interact with the Amazon Bedrock API to perform text generation.
Understand the parameters and options available for text generation.
Analyze and interpret the generated text, identifying potential improvements.
Introduction to Mistral Foundational Models on Amazon Bedrock3:47
In this lecture, we will learn on what are Mistral Foundational models on Amazon Bedrock.
Using Mistral Through the Amazon Bedrock API4:18
In this lecture, we will learn how to use Mistral through the Amazon Bedrock API.
An Introduction to using Stability AI6:09
Explore Stability AI and its integration with Amazon Bedrock. This lecture covers how Stability AI can enhance the reliability and robustness of generative models within Bedrock.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the fundamentals of Stability AI.
Recognize how Stability AI integrates with Amazon Bedrock to enhance model reliability.
Apply Stability AI techniques to improve the robustness of their generative AI applications.
Using Stability AI Through the Amazon Bedrock API16:43
This lecture dives into the practical aspect of utilizing Stability AI through the Amazon Bedrock API. Through hands-on exercises, students will learn how to implement Stability AI techniques to enhance their models.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Utilize Stability AI through the Amazon Bedrock API.
Implement Stability AI techniques in their projects.
Evaluate the impact of Stability AI on model performance and reliability.
Generating Code on the Amazon Bedrock API15:21
Explore the fascinating capability of generating code using the Amazon Bedrock API. This lecture covers how to input descriptive prompts and obtain generated code snippets, alongside understanding the underlying principles of code generation.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Generate code snippets using the Amazon Bedrock API.
Understand the principles behind code generation using generative models.
Evaluate the quality of generated code and iterate on prompts to improve outcomes.
End of Section 3 Quiz

Multimodal Models with Claude 3 on Amazon Bedrock4:18
In this lecture, we will learn what multimodal models are and how Claude 3 is one of them on Amazon Bedrock.
Experimenting with Claude 3 Sonnet on the Amazon Bedrock Playground4:30
In this lecture, we will look at how to use Claude 3 Sonnet's text and vision capabilities through the Amazon Bedrock Playground.
Working with Claude 3 Sonnet through the Amazon Bedrock API6:59
In this lecture, we will look at how to use Claude 3 Sonnet's text and vision capabilities through the API.
Exploring the Capabilities of Claude 3.5 Sonnet v2 Through the Playground6:54
Explore the capabilities of Anthropic's newest model from the Claude family; Claude 3.5 Sonnet v2.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand how to start prompting Claude 3.5 Sonnet v2
Understand how to use the model to prompt and ask about diagrams and have it create code.
Explore more themselves on how they can best make use of the model for their use cases.
Introduction to Creating Context-driven Chatbots8:45
Explore the exciting realm of context-driven chatbots in this introductory lecture. Learn about the importance of context in conversational AI, various approaches to context management, and the benefits of context-aware chatbots.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the significance of context in conversational AI.
Recognize various approaches to context management.
Appreciate the benefits of context-driven chatbots in enhancing user interaction.
Coding Up a Chatbot with Context on Amazon Bedrock8:32
Delve into the practical aspect of building context-aware chatbots on Amazon Bedrock. This lecture will guide students through the coding process, demonstrating how to manage and utilize context to create engaging conversational experiences.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Code a context-aware chatbot using Amazon Bedrock.
Manage conversation context effectively.
Experiment with different contextual setups to enhance chatbot responsiveness.
Introduction to Retrieval Augmented Generation7:58
Uncover the concept of Retrieval Augmented Generation (RAG) in this enlightening lecture. Learn how RAG combines retrieval and generation to produce more informed and accurate responses in AI applications.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the principles of Retrieval Augmented Generation.
Recognize the benefits of combining retrieval and generation in AI models.
Appreciate the potential applications of RAG in various domains.
Using Retrieval Augmented Generation with Amazon Bedrock API10:02
Explore how to implement Retrieval Augmented Generation using the Amazon Bedrock API. This lecture will provide practical demonstrations and guidelines on utilizing RAG to enhance the capabilities of generative models on Amazon Bedrock.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Implement Retrieval Augmented Generation using Amazon Bedrock API.
Enhance generative models with retrieval capabilities.
Experiment with RAG to improve the quality of generated responses.
End of Section 4 Quiz

Introduction to Building Generative AI Applications with Amazon Bedrock8:14
Step into the world of Generative AI applications with Amazon Bedrock. This lecture introduces the core concepts, tools, and processes involved in building generative AI applications, providing a solid foundation for the practical exercises that follow.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the core concepts of building web applications with generative AI.
Recognize the tools and processes involved in building generative AI applications with Amazon Bedrock.
Appreciate the potential of generative AI in creating innovative applications.
An example of Building a Frontend of an AI Application4:51
Dive into the practical aspect of building the frontend of an AI application. This lecture will showcase a real-world example, guiding students through the design, development, and integration processes to create an intuitive and engaging user interface.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Design and develop the frontend of an AI application.
Integrate the frontend with backend services.
Evaluate and refine the user interface to enhance user experience.
An example of Building a Backend of an AI Application6:02
Explore the intricacies of building the backend of an AI application. Through a real-world example, this lecture will guide students through the development, deployment, and management of backend services to support AI functionalities.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Develop, deploy, and manage backend services of an AI application.
Integrate backend services with frontend and other system components.
Ensure the scalability, reliability, and security of the backend infrastructure.
Setting Up the Lambda and API Gateway4:13
Discover the process of setting up AWS Lambda and API Gateway to support your AI application. This lecture will provide practical guidelines on configuring, deploying, and managing serverless functions and API endpoints.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Configure and deploy AWS Lambda functions.
Set up API Gateway to manage API endpoints.
Integrate Lambda and API Gateway to support AI application functionalities.
A Demo of an AI Application3:14
Experience a live demonstration of an AI web application built over the course. This lecture will showcase the entire system in action, providing insights into how all the components work together to deliver AI-powered functionalities.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the complete workflow of the AI web application.
Recognize how the frontend, backend, and AI services interact.
Appreciate the practical implementation of concepts learned throughout the course.
End of Section 5 Quiz

Best Practices5:50
Delve into the best practices for utilizing Amazon Bedrock effectively. This lecture covers tips and guidelines on managing resources, optimizing performance, and ensuring security and compliance in your AI projects.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Apply best practices in managing resources and optimizing performance on Amazon Bedrock.
Ensure security and compliance in their AI projects.
Recognize common pitfalls and how to avoid them when using Amazon Bedrock.
Pricing5:18
Uncover the pricing model of Amazon Bedrock and learn how to manage costs effectively. This lecture will provide an overview of the pricing structure, cost management tools, and tips on optimizing costs for your AI projects.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Understand the pricing model of Amazon Bedrock.
Utilize cost management tools to monitor and control expenses.
Apply tips and guidelines to optimize costs in their AI projects.
Challenges3:02
Discuss the challenges encountered in building AI applications with Amazon Bedrock. This lecture will explore common issues, potential solutions, and how to overcome hurdles in your AI development journey.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Recognize common challenges in building AI applications with Amazon Bedrock.
Apply problem-solving techniques to overcome hurdles.
Share experiences and solutions with peers to foster collaborative learning.
Where to Go from Here2:08
Explore the next steps in your AI development journey with Amazon Bedrock. This lecture will provide resources, communities, and avenues for further learning and exploration beyond the course.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Identify resources and communities for further learning and collaboration.
Plan their next steps in advancing their skills and projects with Amazon Bedrock.
Engage in continuous learning to stay updated with the evolving landscape of AI on Amazon Bedrock.
End of Section 6 Quiz

A Customer Service Representative Project
Final Exam
Wrap-Up5:15
Reflect on the key learnings, accomplishments, and experiences garnered over the course. This wrap-up lecture will summarize the core concepts, practical skills acquired, and the journey ahead in building innovative AI applications with Amazon Bedrock.
Learning Outcomes:
Upon completing this lecture, students will be able to:
Reflect on the key learnings and accomplishments over the course.
Recognize the practical skills acquired in building AI applications with Amazon Bedrock.
Look forward to the journey ahead in creating innovative AI solutions and contributing to the AI community.
Bonus Lecture3:17
Other resources from the instructor that would be helpful in your Amazon Bedrock learning journey.

Requisitos

Proficiency in Python programming is essential.
A foundational understanding of artificial intelligence and machine learning concepts will be beneficial to grasp the advanced topics covered in the course.
A computer with internet access to utilize AWS services and to participate in the online course.
An AWS account to practice and apply the concepts learned in the course.

Descripción

Embark on a transformative learning journey with the "Amazon Bedrock Masterclass: A Guide to Generative AI on AWS." This comprehensive course is meticulously designed to equip you with the knowledge, skills, and practical expertise necessary to excel in the rapidly evolving domain of Generative Artificial Intelligence (AI) using Amazon's robust Bedrock platform.

The course commences with a robust introduction to Amazon Bedrock, providing a solid foundation to understand the platform's capabilities and offerings. As a fully managed service on AWS, Amazon Bedrock simplifies the development of generative AI applications by providing access to high-performing foundation models from leading AI companies. This initial module sets the stage for the subsequent exploration and deep-dive into the myriad features that Amazon Bedrock offers, thus enabling a nuanced understanding and effective utilization of the platform. As you progress through the course, you'll delve into the technical intricacies of working with the Amazon Bedrock API, a critical skill for leveraging the platform's capabilities to the fullest. Your learning journey will then advance to exploring the advanced features of Amazon Bedrock, thus preparing you to handle complex generative AI projects.

The course then transitions to a practical approach, focusing on building generative AI applications with Bedrock. This module is designed to transition theoretical knowledge into practical expertise, enabling you to conceptualize, develop, and deploy generative AI applications effectively. Afterwards, the course underscores the importance of adhering to best practices while also providing a thorough understanding of the pricing model of Amazon Bedrock, thereby enabling informed and cost-effective decision-making.

The culmination of this masterclass is the Capstone Project, where you'll apply the amassed knowledge and skills in a real-world project, showcasing your competency in utilizing Amazon Bedrock for generative AI applications. This hands-on project is an opportunity to integrate and apply the learning from each module in a practical scenario, thus solidifying your understanding and readiness to tackle real-world generative AI challenges using Amazon Bedrock.

The "Amazon Bedrock Masterclass: A Guide to Generative AI on AWS" is more than just a course; it's a pathway to mastering generative AI on one of the most sophisticated platforms. The structured modules, practical insights, and the capstone project collectively ensure a rich, engaging, and rewarding learning experience, propelling you towards becoming a proficient practitioner of generative AI on AWS.

¿Para quién es este curso?

Developers and AI Practitioners
Data Scientists and Machine Learning Engineers
AI Enthusiasts and Hobbyists
Technical Managers and Decision-makers
Educators and AI Researchers

Lo que aprenderás

Ver temas relacionados

Contenido del curso

Introduction to Amazon Bedrock5 clases • 31 min

Exploring Bedrock Features7 clases • 25 min

Working with the Amazon Bedrock API10 clases • 1 h 18 min

Advanced Features with Amazon Bedrock8 clases • 58 min

Building Generative AI Applications with Bedrock5 clases • 27 min

Best Practices and Pricing4 clases • 16 min

Capstone Project and Final Exam2 clases • 9 min

Requisitos

Descripción

¿Para quién es este curso?