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Noah Gift,
Alfredo Deza,
and
Derek Wales
This course will teach you how to deploy and manage large language models (LLMs) in production using AWS services like Amazon Bedrock. By the end of the course, you will know how to:
Choose the right LLM architecture and model for your application using services.
Optimize cost, performance and scalability of LLMs on AWS using auto-scaling groups, spot instances and container orchestration
Monitor and log metrics from your LLM to detect issues and continuously improve quality
Build reliable and secure pipelines to train, deploy and update models using AWS services
Read more
This course will teach you how to deploy and manage large language models (LLMs) in production using AWS services like Amazon Bedrock. By the end of the course, you will know how to:
Choose the right LLM architecture and model for your application using services.
Optimize cost, performance and scalability of LLMs on AWS using auto-scaling groups, spot instances and container orchestration
Monitor and log metrics from your LLM to detect issues and continuously improve quality
Build reliable and secure pipelines to train, deploy and update models using AWS services
Read more
This course will teach you how to deploy and manage large language models (LLMs) in production using AWS services like Amazon Bedrock. By the end of the course, you will know how to:
Choose the right LLM architecture and model for your application using services.
Optimize cost, performance and scalability of LLMs on AWS using auto-scaling groups, spot instances and container orchestration
Monitor and log metrics from your LLM to detect issues and continuously improve quality
Build reliable and secure pipelines to train, deploy and update models using AWS services
Comply with regulations when deploying LLMs in production through techniques like differential privacy and controlled rollouts
This course is unique in its focus on real-world operationalization of large language models using AWS. You will work through hands-on labs to put concepts into practice as you learn. Whether you are a machine learning engineer, data scientist or technical leader, you will gain practical skills to run LLMs in production.
Register for this course and see more details by visiting:
OpenCourser.com/course/j0g4v3/genai
What's inside
Syllabus
Getting Started with Developing on AWS for AI
This week, you will learn how to set up a Rust development environment, utilize the AWS SDK for Rust, and build AWS Lambda functions with Rust.
Read more
Syllabus
Getting Started with Developing on AWS for AI
This week, you will learn how to set up a Rust development environment, utilize the AWS SDK for Rust, and build AWS Lambda functions with Rust.
Read more
AI Pair Programming from CodeWhisperer to Prompt Engineering
CodeWhisperer writes code. You learn to guide it. Large language models crunch data, spit out content. Chain-of-thought prompts make models explain themselves. Craft prompts, shape outputs. Build CLI tools, bash functions. Use CodeWhisperer CLI to automate tasks. Fast, efficient coding with AI.
Amazon Bedrock
This week, learn Amazon Bedrock capabilities. Apply through model evaluations and customizations.
Project Challenges
This week, you will challenge yourself to apply the concepts covered in the previous week and challenge yourself to apply what you learned in a new context.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches skills and knowledge that are highly relevant to industry
Develops advanced professional skills in deploying and managing large language models
Taught by Noah Gift, Alfredo Deza, and Derek Wales, who are recognized for their work in large language models
Covers topics that are unique to the industry
Offers hands-on labs and interactive materials to enhance learning
Requires learners to come in with ample background knowledge and experience
Save this course
Save GenAI and LLMs on AWS 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 GenAI and LLMs on AWS with these
activities:
Read Deep Learning with Python
Show steps
Strengthen your understanding of deep learning concepts and techniques with this recommended reading
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the book's introduction and first chapter
-
Complete the exercises in the book's first chapter
-
Summarize the key concepts covered in the book's first chapter
Review Rust
Show steps
Reinforce your foundational understanding of Rust to better prepare for this course's material
Browse courses on
Rust
Show steps
-
Review the Rust documentation
-
Complete the Rustlings tutorial
-
Build a simple Rust project
Review AWS Lambda functions
Show steps
Strengthen your foundational knowledge of AWS Lambda functions to better prepare for this course's material
Browse courses on
AWS Lambda
Show steps
-
Review the AWS Lambda documentation
-
Complete the AWS Lambda tutorial
-
Build a simple AWS Lambda function
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Amazon Bedrock tutorials
Show steps
Enhance your understanding of Amazon Bedrock through guided tutorials
Browse courses on
Amazon Bedrock
Show steps
-
Complete the Amazon Bedrock getting started tutorial
-
Complete the Amazon Bedrock advanced tutorial
-
Build a simple Amazon Bedrock application
Attend an LLM workshop
Show steps
Enhance your knowledge and skills in LLM deployment and management through a workshop
Browse courses on
Large Language Models
Show steps
-
Identify and register for an LLM workshop
-
Attend the workshop and actively participate in the activities
-
Implement what you learned from the workshop in your own projects
Practice deploying and managing LLMs
Show steps
Develop your practical skills in deploying and managing LLMs
Browse courses on
Large Language Models
Show steps
-
Deploy a simple LLM using Amazon Bedrock
-
Manage the LLM's resources and performance
-
Monitor the LLM's usage and identify areas for improvement
Write a blog post on LLM deployment
Show steps
Solidify your understanding of LLM deployment by sharing your knowledge through writing
Show steps
-
Choose a specific aspect of LLM deployment to focus on
-
Research the topic and gather relevant information
-
Write a clear and concise blog post that explains the topic
Read Deep Learning with Python
Show steps
Strengthen your understanding of deep learning concepts and techniques with this recommended reading
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter
- Complete the exercises in the book's first chapter
- Summarize the key concepts covered in the book's first chapter
Review Rust
Show steps
Reinforce your foundational understanding of Rust to better prepare for this course's material
Browse courses on
Rust
Show steps
Show steps
Show steps
- Review the Rust documentation
- Complete the Rustlings tutorial
- Build a simple Rust project
Review AWS Lambda functions
Show steps
Strengthen your foundational knowledge of AWS Lambda functions to better prepare for this course's material
Browse courses on
AWS Lambda
Show steps
Show steps
Show steps
- Review the AWS Lambda documentation
- Complete the AWS Lambda tutorial
- Build a simple AWS Lambda function
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Amazon Bedrock tutorials
Show steps
Enhance your understanding of Amazon Bedrock through guided tutorials
Browse courses on
Amazon Bedrock
Show steps
Show steps
Show steps
- Complete the Amazon Bedrock getting started tutorial
- Complete the Amazon Bedrock advanced tutorial
- Build a simple Amazon Bedrock application
Attend an LLM workshop
Show steps
Enhance your knowledge and skills in LLM deployment and management through a workshop
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
- Identify and register for an LLM workshop
- Attend the workshop and actively participate in the activities
- Implement what you learned from the workshop in your own projects
Practice deploying and managing LLMs
Show steps
Develop your practical skills in deploying and managing LLMs
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
- Deploy a simple LLM using Amazon Bedrock
- Manage the LLM's resources and performance
- Monitor the LLM's usage and identify areas for improvement
Write a blog post on LLM deployment
Show steps
Solidify your understanding of LLM deployment by sharing your knowledge through writing
Show steps
Show steps
Show steps
- Choose a specific aspect of LLM deployment to focus on
- Research the topic and gather relevant information
- Write a clear and concise blog post that explains the topic
Career center
Learners who complete GenAI and LLMs on AWS will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
The GenAI and LLMs on AWS course develops foundational capabilities for AI Engineers. It goes into detail on the operationalization of large language models using AWS. The deployment and management skills are invaluable to the efficient scaling of AI projects.
Natural Language Processing Engineer
Natural Language Processing Engineer
The GenAI and LLMs on AWS course is highly relevant to Natural Language Processing Engineers as it delves into the practicalities of deploying and managing large language models on AWS, essential skills for this specialized role.
Site Reliability Engineer
Site Reliability Engineer
The GenAI and LLMs on AWS course is an excellent choice for Site Reliability Engineers looking to deepen their understanding of deploying and managing AI models in production. The focus on reliability, scalability, and monitoring will be especially beneficial.
AI Researcher
AI Researcher
The GenAI and LLMs on AWS course provides AI Researchers with a practical understanding of deploying and managing large language models on AWS, which can support their research and innovation in the field.
Cloud Architect
Cloud Architect
GenAI and LLMs on AWS equips learners with the knowledge necessary to make informed decisions about the deployment of AI and ML models in AWS environments. This course can help Cloud Architects specializing in ML gain expertise in optimizing and scaling LLM deployments on AWS.
Machine Learning Scientist
Machine Learning Scientist
The GenAI and LLMs on AWS course can empower Machine Learning Scientists with the knowledge and expertise to deploy and manage large language models on AWS, a valuable addition to their skillset.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for the development and deployment of AI models. By taking this course, individuals will learn best practices, techniques, and tools for deploying and managing large language models on AWS.
Data Engineer
Data Engineer
The GenAI and LLMs on AWS course is designed to provide Data Engineers interested in leveraging large language models with the tools and practices to successfully build and manage these models on AWS.
Data Science Manager
Data Science Manager
The GenAI and LLMs on AWS course provides Data Science Managers with crucial insights into the deployment and management of large language models on AWS, enabling them to effectively lead and manage teams in this domain.
DevOps Engineer
DevOps Engineer
The GenAI and LLMs on AWS course provides DevOps Engineers with valuable knowledge for integrating AI and ML models into their continuous integration and deployment pipelines.
Data Scientist
Data Scientist
Data Scientists play a key role in the development and deployment of AI models. The GenAI and LLMs on AWS course provides a strong foundation for Data Scientists to apply their knowledge to large language models.
Data Analyst
Data Analyst
The GenAI and LLMs on AWS course can provide Data Analysts with a solid foundation in deploying and managing large language models on AWS, which can enhance their capabilities in data analysis and modeling.
Software Engineer
Software Engineer
The GenAI and LLMs on AWS course will help Software Engineers gain expertise in deploying and managing large language models on AWS. This knowledge can be applied to various industries and projects.
Product Manager
Product Manager
Product Managers involved in AI or ML products may find the GenAI and LLMs on AWS course beneficial in understanding the technical aspects of deploying and managing large language models on AWS.
Business Analyst
Business Analyst
Business Analysts may find the GenAI and LLMs on AWS course useful for understanding the potential of large language models and how they can be applied to business problems.
For more career information including salaries, visit:
OpenCourser.com/course/j0g4v3/genai
Reading list
We've selected 13 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
GenAI and LLMs on AWS.
Provides a comprehensive introduction to recurrent neural networks (RNNs) for natural language processing (NLP).
Provides a comprehensive overview of deep learning with Python. It covers topics such as the history of deep learning, different types of deep learning models, and applications of deep learning.
Provides a hands-on introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers topics such as data preparation, model training, and deployment.
Provides a comprehensive overview of data science. It covers topics such as data collection, data analysis, and data visualization.
Provides a comprehensive overview of statistical learning. It covers topics such as data analysis, model selection, and prediction.
Provides a less technical introduction to statistical learning. It covers topics such as data analysis, model selection, and prediction.
Provides a probabilistic perspective on machine learning. It covers topics such as Bayes' theorem, graphical models, and Markov chain Monte Carlo.
Provides a comprehensive overview of Gaussian processes for machine learning. It covers topics such as the history of Gaussian processes, different types of Gaussian processes.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers topics such as the history of Bayesian reasoning, different types of Bayesian models, and applications of Bayesian reasoning.
Provides a hands-on introduction to machine learning for hackers. It covers topics such as data collection, data analysis, and model training.
Save
Provides a comprehensive overview of data mining. It covers topics such as data collection, data analysis, and model training.
Provides a hands-on introduction to machine learning. It covers topics such as data collection, data analysis, and model training.
Save
Provides a comprehensive overview of deep learning. It covers topics such as the history of deep learning, different types of deep learning models, and applications of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/j0g4v3/genai
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Effort
4 weeks of study, 5 hours/week
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
Derek Wales
Language
English
This course belongs to a
collection
called
Large Language Model Operations (LLMOps). It's comprised of five courses:
- Databricks to Local LLMs
- Open Source LLMOps Solutions
- Operationalizing LLMs on Azure
- GenAI and LLMs on AWS (viewing)
- Advanced Data Engineering
Good to know
Teaches skills and knowledge that are highly relevant to industry
Develops advanced professional skills in deploying and managing large language models
Taught by Noah Gift, Alfredo Deza, and Derek Wales, who are recognized for their work in large language models
Covers topics that are unique to the industry
Offers hands-on labs and interactive materials to enhance learning
Requires learners to come in with ample background knowledge and experience
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