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Faye Ellis
Get started with Amazon Bedrock and learn how to integrate it with your app.
Amazon Bedrock is the easiest way to build and scale generative AI applications with foundational models. In this demo, Principal AWS Training Architect, Faye Ellis, explains how to use this fully managed service step by step, from selecting a model to integrating it into your custom app. From chatbots to data searches to image generation, the sky’s the limit for what you can create with Bedrock.
Register for this course and see more details by visiting:
OpenCourser.com/course/l5yj2y/how
What's inside
Syllabus
How to Add GenAI Capabilities to Your App Code Using Amazon Bedrock
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Beginner-friendly
Introduces Amazon Bedrock and its integration with apps
Provides hands-on experience with the service
Taught by a Principal AWS Training Architect
Focuses on practical application in building and scaling generative AI applications
Covers a wide range of applications, including chatbots, data searches, and image generation
Save this course
Save How to Add GenAI Capabilities to Your App Code Using Amazon Bedrock to your list so you can find it easily later:
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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 How to Add GenAI Capabilities to Your App Code Using Amazon Bedrock with these
activities:
Review Machine Learning Fundamentals
Show steps
Refresh foundational concepts in machine learning such as supervised learning, unsupervised learning, and model evaluation to prepare for this course.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Read through course syllabus and textbooks to identify key concepts
-
Review lecture notes or online resources on machine learning basics
-
Work through practice problems or exercises to test understanding
Gather AI Tools and Resources
Show steps
Compile a collection of useful tools and resources related to AI and machine learning, such as libraries, frameworks, and online communities.
Show steps
-
Research and identify relevant tools and resources
-
Create a document or spreadsheet to organize the information
-
Include links, descriptions, and any relevant documentation
-
Share the compilation with others or use it for future reference
Follow Tutorials on Bedrock Basics
Show steps
Seek out tutorials that provide step-by-step guidance on using Amazon Bedrock, such as those offered by AWS or other online platforms.
Show steps
-
Search for tutorials on Bedrock usage and getting started
-
Follow the instructions and practice using Bedrock features
-
Experiment with different models and scenarios
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Integrating Bedrock with Your App
Show steps
Engage in hands-on exercises that involve integrating Amazon Bedrock with your own custom app, reinforcing the practical application of the concepts taught in the course.
Show steps
-
Create a simple app using your preferred programming language
-
Follow the Bedrock documentation to integrate the service
-
Test the app and troubleshoot any issues
-
Modify the app to explore different integration options
Build an AI-Powered Chatbot
Show steps
Apply your knowledge of Amazon Bedrock by creating a functional chatbot that leverages generative AI capabilities to engage with users.
Show steps
-
Design the chatbot's functionality and user interface
-
Choose an appropriate Bedrock model for the chatbot's purpose
-
Integrate Bedrock with your chatbot application
-
Train and fine-tune the chatbot using training data
-
Deploy and test the chatbot
Participate in an AI Hackathon
Show steps
Challenge yourself by participating in an AI hackathon focused on generative AI or Bedrock to demonstrate and improve your skills in a competitive environment.
Show steps
-
Find an appropriate AI hackathon to participate in
-
Form a team or work individually
-
Develop an innovative AI solution using Bedrock
-
Present your solution and compete with other teams
Discuss Advanced Use Cases of Bedrock
Show steps
Engage with peers to explore advanced applications of Amazon Bedrock, such as building multi-modal AI systems or integrating Bedrock with other AI services.
Show steps
-
Find a study group or online community focused on AI and Bedrock
-
Participate in discussions and share your experiences
-
Collaborate on projects or ideas
Review Machine Learning Fundamentals
Show steps
Refresh foundational concepts in machine learning such as supervised learning, unsupervised learning, and model evaluation to prepare for this course.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Read through course syllabus and textbooks to identify key concepts
- Review lecture notes or online resources on machine learning basics
- Work through practice problems or exercises to test understanding
Gather AI Tools and Resources
Show steps
Compile a collection of useful tools and resources related to AI and machine learning, such as libraries, frameworks, and online communities.
Show steps
Show steps
Show steps
- Research and identify relevant tools and resources
- Create a document or spreadsheet to organize the information
- Include links, descriptions, and any relevant documentation
- Share the compilation with others or use it for future reference
Follow Tutorials on Bedrock Basics
Show steps
Seek out tutorials that provide step-by-step guidance on using Amazon Bedrock, such as those offered by AWS or other online platforms.
Show steps
Show steps
Show steps
- Search for tutorials on Bedrock usage and getting started
- Follow the instructions and practice using Bedrock features
- Experiment with different models and scenarios
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Integrating Bedrock with Your App
Show steps
Engage in hands-on exercises that involve integrating Amazon Bedrock with your own custom app, reinforcing the practical application of the concepts taught in the course.
Show steps
Show steps
Show steps
- Create a simple app using your preferred programming language
- Follow the Bedrock documentation to integrate the service
- Test the app and troubleshoot any issues
- Modify the app to explore different integration options
Build an AI-Powered Chatbot
Show steps
Apply your knowledge of Amazon Bedrock by creating a functional chatbot that leverages generative AI capabilities to engage with users.
Show steps
Show steps
Show steps
- Design the chatbot's functionality and user interface
- Choose an appropriate Bedrock model for the chatbot's purpose
- Integrate Bedrock with your chatbot application
- Train and fine-tune the chatbot using training data
- Deploy and test the chatbot
Participate in an AI Hackathon
Show steps
Challenge yourself by participating in an AI hackathon focused on generative AI or Bedrock to demonstrate and improve your skills in a competitive environment.
Show steps
Show steps
Show steps
- Find an appropriate AI hackathon to participate in
- Form a team or work individually
- Develop an innovative AI solution using Bedrock
- Present your solution and compete with other teams
Discuss Advanced Use Cases of Bedrock
Show steps
Engage with peers to explore advanced applications of Amazon Bedrock, such as building multi-modal AI systems or integrating Bedrock with other AI services.
Show steps
Show steps
Show steps
- Find a study group or online community focused on AI and Bedrock
- Participate in discussions and share your experiences
- Collaborate on projects or ideas
Career center
Learners who complete How to Add GenAI Capabilities to Your App Code Using Amazon Bedrock will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. With Amazon Bedrock, add GenAI capabilities to the software applications you build, boosting your productivity and creating more robust and responsive systems. This course provides a foundation in working with foundational models, which will enhance your skills as a Software Engineer.
Data Scientist
Data Scientist
Data Scientists use their expertise in machine learning and AI to analyze data and extract insights. This course on Amazon Bedrock can provide valuable knowledge for Data Scientists, as it delves into working with generative AI applications and integrating them with custom apps. Gaining proficiency in these skills can enhance your ability to develop and deploy innovative data-driven solutions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. Amazon Bedrock is a fully managed service that simplifies the process of adding generative AI capabilities to applications. This course provides a step-by-step guide on how to use Bedrock, enabling Machine Learning Engineers to seamlessly integrate GenAI into their projects and drive innovation.
AI Engineer
AI Engineer
AI Engineers design, develop, and implement AI systems. This course on Amazon Bedrock provides valuable insights into working with foundational models and integrating them into custom applications. These skills are crucial for AI Engineers who want to stay ahead in the field and create cutting-edge AI solutions.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. Amazon Bedrock can be a valuable tool for Product Managers who want to add AI capabilities to their products. This course provides a comprehensive understanding of how to use Bedrock and its benefits, empowering Product Managers to make informed decisions and drive product innovation.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to provide insights for decision-making. This course on Amazon Bedrock may be helpful for Data Analysts who want to expand their skills in working with AI and generative models. Gaining proficiency in these areas can enhance their ability to extract valuable insights from data and contribute to data-driven decision-making.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course on Amazon Bedrock provides a solid foundation for Software Developers who want to integrate AI capabilities into their projects. By learning how to use Bedrock, Developers can enhance the functionality and user experience of their applications, making them more competitive in the market.
Web Developer
Web Developer
Web Developers design and develop websites and web applications. This course on Amazon Bedrock may be helpful for Web Developers who want to explore the integration of generative AI into their projects. Gaining proficiency in using Bedrock can enable Web Developers to create more interactive and engaging web experiences, enhancing user engagement and satisfaction.
UX Designer
UX Designer
UX Designers focus on creating user-centered experiences for websites and applications. This course on Amazon Bedrock can provide valuable insights for UX Designers who want to incorporate AI into their designs. By learning how to use Bedrock, UX Designers can enhance the usability and accessibility of their designs, ultimately improving the overall user experience.
IT Architect
IT Architect
IT Architects design and maintain IT systems. This course on Amazon Bedrock may be helpful for IT Architects who want to stay updated with the latest advancements in AI and generative models. Gaining proficiency in using Bedrock can enable IT Architects to design and implement robust and scalable IT systems that meet the evolving needs of businesses.
Cloud Architect
Cloud Architect
Cloud Architects design and implement cloud solutions. This course on Amazon Bedrock may be useful for Cloud Architects who want to explore the integration of generative AI into their cloud architectures. Gaining proficiency in using Bedrock can enable Cloud Architects to create innovative and cost-effective cloud solutions that leverage the power of AI.
DevOps Engineer
DevOps Engineer
DevOps Engineers bridge the gap between development and operations teams. This course on Amazon Bedrock may be helpful for DevOps Engineers who want to streamline the deployment and management of AI applications. Gaining proficiency in using Bedrock can enable DevOps Engineers to automate the deployment process and ensure the reliability and scalability of AI applications.
AI Researcher
AI Researcher
AI Researchers explore and develop new methods and algorithms for AI. This course on Amazon Bedrock may be helpful for AI Researchers who want to stay updated with the latest advancements in generative AI models. Gaining proficiency in using Bedrock can enable AI Researchers to experiment with new models and contribute to the development of cutting-edge AI technologies.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. This course on Amazon Bedrock may be helpful for Data Engineers who want to explore the integration of generative AI into their data pipelines. Gaining proficiency in using Bedrock can enable Data Engineers to create more efficient and scalable data pipelines that can handle the increasing volume and complexity of data.
Technical Writer
Technical Writer
Technical Writers create documentation and training materials for software and technology products. This course on Amazon Bedrock may be helpful for Technical Writers who want to learn about the latest advancements in AI and generative models. Gaining proficiency in using Bedrock can enable Technical Writers to create more comprehensive and informative documentation that helps users understand and use AI technologies effectively.
For more career information including salaries, visit:
OpenCourser.com/course/l5yj2y/how
Reading list
We've selected 11 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
How to Add GenAI Capabilities to Your App Code Using Amazon Bedrock.
This practical guide to deep reinforcement learning covers the fundamentals and provides hands-on experience with building and training reinforcement learning models. It valuable resource for those interested in building generative AI models for decision-making and control tasks.
Save
Explores the potential of AI to enhance human capabilities, discussing topics such as natural language processing, computer vision, and machine learning. It provides insights into how AI can be used to improve our lives and work.
This foundational textbook on reinforcement learning provides a comprehensive overview of the field. It valuable resource for understanding the principles and algorithms behind generative AI models for decision-making and control tasks.
This practical guide to applied statistical learning provides a hands-on approach to building and evaluating statistical models. It useful resource for understanding the practical aspects of generative AI model development.
This comprehensive textbook on speech and language processing provides a foundation in natural language processing and speech recognition. It is useful for understanding the fundamentals of generative AI models for language generation and speech synthesis.
This practical guide to machine learning with popular libraries provides a hands-on approach to building and deploying machine learning models. It valuable resource for those interested in implementing generative AI models using Python libraries.
This practical guide to deep learning with Fastai and PyTorch covers the fundamentals of deep learning and provides hands-on experience with building and training models. It good resource for those interested in implementing generative AI models using these tools.
This classic textbook on statistical learning provides a solid foundation in machine learning and statistical modeling. It useful reference for understanding the mathematical and statistical principles behind generative AI models.
Save
This comprehensive book on computer vision provides a solid foundation in image processing, feature extraction, and object recognition. It is helpful for understanding the principles behind generative AI models for image generation and manipulation.
This introductory textbook offers a solid foundation in deep learning concepts, including artificial neural networks. It provides a good starting point for developers seeking to understand the theoretical basis of generative AI models.
This seminal paper introduces GANs, one of the most significant generative AI models. It provides a detailed explanation of GANs and their theoretical underpinnings, making it a valuable resource for researchers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l5yj2y/how
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Time
4 hours
Level
Introductory
Via
Pluralsight
Instructor
Faye Ellis
Language
English
Good to know
Beginner-friendly
Introduces Amazon Bedrock and its integration with apps
Provides hands-on experience with the service
Taught by a Principal AWS Training Architect
Focuses on practical application in building and scaling generative AI applications
Covers a wide range of applications, including chatbots, data searches, and image generation
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