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Comet
Unlock the power of Large Language Models (LLMs) with Udacity's free LLMOps course. Learn to build real-world applications with LLMs using the latest tools and techniques in the field.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Basic machine learning
- Intermediate Python
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/lf5016/llmops
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What's inside
Syllabus
In this lesson, we will introduce LLMs and LLMOps, discuss the importance of LLMOps for real-world applications, overview the LLMOps lifecycle, and explain the difference between LLMOps and MLOps.
Read more
Syllabus
In this lesson, we will introduce LLMs and LLMOps, discuss the importance of LLMOps for real-world applications, overview the LLMOps lifecycle, and explain the difference between LLMOps and MLOps.
Read more
In this lesson, we will strategize around model training and selection, fine-tune and improve LLMs with experiment tracking, revise evaluation approaches for LLMs, and explore prompt engineering.
In this lesson, we will learn about model versioning and experiment management, explore different strategies for debugging LLMs, and deploy, monitor, and maintain LLMs in production.
In this lesson, we will explore several real world applications of LLMs, build a reliable customer support chatbot, build an LLM-based evaluation system, and implement a clickbait detector.
In this lesson, we will explore challenges and strategies pertaining to running LLMs at scale, dive into safety and privacy concerns in AI, and learn about adversarial prompting and AI security.
In this lesson, we will take a high-level view of LLMOps trends, look towards the future of LLMs and LLMOps, and explore the broader MLOps landscape.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a comprehensive overview of the LLMOps lifecycle, empowering learners to navigate the development and deployment of LLMs effectively
Emphasizes the importance of LLMOps for real-world applications, ensuring learners understand the practical significance of LLMs in various domains
Covers strategies for model training, fine-tuning, and evaluation, equipping learners with the skills to optimize LLM performance
Focuses on debugging and maintenance techniques for LLMs, enabling learners to address potential issues and ensure smooth operation of LLM-based systems
Explored applications of LLMs, showcasing their versatility and potential for solving real-world problems
Requires prior knowledge in basic machine learning and intermediate Python, making it suitable for learners with some foundational understanding of these areas
Save this course
Save LLMOps: Building Real-World Applications With Large Language Models 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 LLMOps: Building Real-World Applications With Large Language Models with these
activities:
Review advanced Python programming
Show steps
Strengthen your Python skills to confidently navigate the technical aspects of LLMOps.
Browse courses on
Python Programming
Show steps
-
Review advanced Python syntax, including lambda functions, generators, and list comprehensions.
-
Familiarize yourself with popular Python libraries used in LLMOps, such as Transformers and Hugging Face.
Experiment with Different LLMs
Show steps
Experimenting with different LLMs will help you understand their strengths and weaknesses, and how to choose the right LLM for your project.
Browse courses on
Large Language Models
Show steps
-
Choose a task or project to build
-
Research the different types of LLMs and their capabilities
-
Experiment with different LLMs, tweaking their parameters and inputs
-
Evaluate the results of your experiments and fine-tune your approach
Practice LLM prompt engineering
Show steps
Practice writing effective prompts to elicit the desired responses from LLMs and enhance the outputs you receive.
Show steps
-
Write prompts for specific tasks, clearly defining the goals and desired outputs.
-
Experiment with different prompt formats and structures to observe the impact on LLM responses.
-
Analyze LLM responses and refine prompts to improve the quality and relevance of outputs.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Complete LLMoPS tutorials
Show steps
Supplement your understanding of LLMOps with interactive, hands-on tutorials.
Browse courses on
LLMs
Show steps
-
Follow online tutorials provided by the course instructors or reputable sources to reinforce LLMOps concepts.
-
Complete practice exercises and assignments to test your comprehension.
Join study groups and forums
Show steps
Connect with fellow learners and experts to exchange knowledge, share experiences, and clarify concepts.
Show steps
-
Join online study groups or forums dedicated to LLMOps to ask questions, share insights, and collaborate.
-
Participate in discussions, contribute to threads, and engage with others to enhance your understanding.
Build an LLMOps resources guide
Show steps
Curate a comprehensive collection of relevant articles, tutorials, and tools for future reference and deeper exploration.
Show steps
-
Search and gather high-quality resources related to LLMs, LLMOps, and related topics.
-
Organize and categorize resources based on their relevance and usefulness.
-
Consider compiling them into a digital document, website, or repository for easy access.
Assist fellow students
Show steps
Help solidify your understanding by sharing your knowledge and assisting others in their learning journey.
Show steps
-
Offer support to fellow students through forums, online platforms, or informal study groups.
-
Answer questions and clarify concepts to reinforce your own knowledge and understanding.
-
Provide constructive feedback to enhance the learning experience of others.
Participate in LLMOps challenges
Show steps
Test your skills and expand your knowledge by tackling real-world LLMOps challenges.
Show steps
-
Identify and participate in hackathons, competitions, or challenges related to LLMs and LLMOps.
-
Collaborate with others to develop innovative solutions to complex problems.
-
Showcase your expertise and gain valuable experience in a competitive environment.
Attend industry workshops
Show steps
Stay up-to-date with the latest advancements and best practices in LLMOps through dedicated workshops.
Show steps
-
Research and identify industry workshops focused on LLMOps or related topics.
-
Attend workshops led by experts in the field to gain practical insights and hands-on experience.
-
Network with professionals and learn about current trends and applications.
Develop an LLM-based application
Show steps
Apply your LLMOps knowledge and skills to create a tangible solution that leverages the power of LLMs.
Show steps
-
Identify a problem or opportunity that can be addressed using an LLM.
-
Design and develop an LLM-based solution, considering aspects such as data collection, model training, and deployment.
-
Evaluate the performance and effectiveness of your solution.
-
Present your project and findings to the class or a wider audience.
Review advanced Python programming
Show steps
Strengthen your Python skills to confidently navigate the technical aspects of LLMOps.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review advanced Python syntax, including lambda functions, generators, and list comprehensions.
- Familiarize yourself with popular Python libraries used in LLMOps, such as Transformers and Hugging Face.
Experiment with Different LLMs
Show steps
Experimenting with different LLMs will help you understand their strengths and weaknesses, and how to choose the right LLM for your project.
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
- Choose a task or project to build
- Research the different types of LLMs and their capabilities
- Experiment with different LLMs, tweaking their parameters and inputs
- Evaluate the results of your experiments and fine-tune your approach
Practice LLM prompt engineering
Show steps
Practice writing effective prompts to elicit the desired responses from LLMs and enhance the outputs you receive.
Show steps
Show steps
Show steps
- Write prompts for specific tasks, clearly defining the goals and desired outputs.
- Experiment with different prompt formats and structures to observe the impact on LLM responses.
- Analyze LLM responses and refine prompts to improve the quality and relevance of outputs.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Complete LLMoPS tutorials
Show steps
Supplement your understanding of LLMOps with interactive, hands-on tutorials.
Browse courses on
LLMs
Show steps
Show steps
Show steps
- Follow online tutorials provided by the course instructors or reputable sources to reinforce LLMOps concepts.
- Complete practice exercises and assignments to test your comprehension.
Join study groups and forums
Show steps
Connect with fellow learners and experts to exchange knowledge, share experiences, and clarify concepts.
Show steps
Show steps
Show steps
- Join online study groups or forums dedicated to LLMOps to ask questions, share insights, and collaborate.
- Participate in discussions, contribute to threads, and engage with others to enhance your understanding.
Build an LLMOps resources guide
Show steps
Curate a comprehensive collection of relevant articles, tutorials, and tools for future reference and deeper exploration.
Show steps
Show steps
Show steps
- Search and gather high-quality resources related to LLMs, LLMOps, and related topics.
- Organize and categorize resources based on their relevance and usefulness.
- Consider compiling them into a digital document, website, or repository for easy access.
Assist fellow students
Show steps
Help solidify your understanding by sharing your knowledge and assisting others in their learning journey.
Show steps
Show steps
Show steps
- Offer support to fellow students through forums, online platforms, or informal study groups.
- Answer questions and clarify concepts to reinforce your own knowledge and understanding.
- Provide constructive feedback to enhance the learning experience of others.
Participate in LLMOps challenges
Show steps
Test your skills and expand your knowledge by tackling real-world LLMOps challenges.
Show steps
Show steps
Show steps
- Identify and participate in hackathons, competitions, or challenges related to LLMs and LLMOps.
- Collaborate with others to develop innovative solutions to complex problems.
- Showcase your expertise and gain valuable experience in a competitive environment.
Attend industry workshops
Show steps
Stay up-to-date with the latest advancements and best practices in LLMOps through dedicated workshops.
Show steps
Show steps
Show steps
- Research and identify industry workshops focused on LLMOps or related topics.
- Attend workshops led by experts in the field to gain practical insights and hands-on experience.
- Network with professionals and learn about current trends and applications.
Develop an LLM-based application
Show steps
Apply your LLMOps knowledge and skills to create a tangible solution that leverages the power of LLMs.
Show steps
Show steps
Show steps
- Identify a problem or opportunity that can be addressed using an LLM.
- Design and develop an LLM-based solution, considering aspects such as data collection, model training, and deployment.
- Evaluate the performance and effectiveness of your solution.
- Present your project and findings to the class or a wider audience.
Career center
Learners who complete LLMOps: Building Real-World Applications With Large Language Models will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are in extremely high demand, and this course is a perfect fit if you're aiming to work as one. It will help you build a foundation in LLMs and LLMOps, which are essential skills for this role. The course also covers how to deploy, monitor, and maintain LLMs in production, which is a key responsibility of Machine Learning Engineers.
Data Scientist
Data Scientist
Data Scientists use their knowledge of machine learning, statistics, and programming to extract insights from data. This often involves working with LLMs, and this course will help you build a strong foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Data Scientists.
Natural Language Processing Engineer
Natural Language Processing Engineer
NLP Engineers are responsible for developing and maintaining natural language processing systems. This course is a perfect fit if you're aiming to work as an NLP Engineer and want to specialize in LLMs. You'll learn how to train and fine-tune LLMs, and you'll also get experience with LLMOps tools and techniques.
Software Engineer
Software Engineer
Software Engineers who specialize in machine learning are in high demand. This course is a perfect fit if you're aiming to work as one and want to specialize in LLMs. You'll learn how to build real-world applications with LLMs, and you'll also get experience with LLMOps tools and techniques.
AI Engineer
AI Engineer
AI Engineers are responsible for developing and maintaining AI systems. This course is a perfect fit if you're aiming to work as an AI Engineer and want to specialize in LLMs. You'll learn how to build real-world applications with LLMs, and you'll also get experience with LLMOps tools and techniques.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data and statistics to solve business problems. This often involves working with LLMs, and this course will help you build a strong foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Data Analysts.
Product Manager
Product Manager
Product Managers are responsible for developing and launching new products. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Product Managers.
Technical Writer
Technical Writer
Technical Writers are responsible for creating documentation for software and other technical products. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Technical Writers.
Technical Support Engineer
Technical Support Engineer
Technical Support Engineers are responsible for providing technical support to customers. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Technical Support Engineers.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to solve business problems. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Business Analysts.
Consultant
Consultant
Consultants use their knowledge of business and technology to help companies solve problems. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Consultants.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics and statistics to solve financial problems. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Quantitative Analysts.
Researcher
Researcher
Researchers use their knowledge of science and engineering to solve problems. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Researchers.
Sales Engineer
Sales Engineer
Sales Engineers are responsible for selling technical products and services. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Sales Engineers.
Project Manager
Project Manager
Project Managers are responsible for planning and executing projects. This often involves working with LLMs, and this course will help you build a foundation in LLMOps. The course also covers how to evaluate and improve LLMs, which is a key skill for Project Managers.
For more career information including salaries, visit:
OpenCourser.com/course/lf5016/llmops
Reading list
We've selected seven 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
LLMOps: Building Real-World Applications With Large Language Models.
Provides a comprehensive overview of deep learning. This book is commonly used by anyone who wants to learn how to use deep learning.
Provides a comprehensive overview of the Python programming language. This book is commonly used by anyone who wants to learn how to use Python.
Provides a comprehensive overview of the mathematics that is used in machine learning. While it is particularly useful as a textbook at academic institutions, this book is also a valuable reference for industry professionals.
Provides a comprehensive overview of interpretable machine learning. While it is particularly useful as a textbook at academic institutions, this book is also a valuable reference for industry professionals.
Provides a comprehensive overview of machine learning with R. This book is commonly used by anyone who wants to learn how to use R for machine learning.
Provides a comprehensive overview of deep learning with Python. This book is commonly used by anyone who wants to learn how to use Python for deep learning.
Provides a comprehensive overview of natural language processing. This book is commonly used by anyone who wants to learn how to use nlp.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lf5016/llmops
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Effort
11 hours
Via
Udacity
Instructor
Comet
Language
English
Good to know
Provides a comprehensive overview of the LLMOps lifecycle, empowering learners to navigate the development and deployment of LLMs effectively
Emphasizes the importance of LLMOps for real-world applications, ensuring learners understand the practical significance of LLMs in various domains
Covers strategies for model training, fine-tuning, and evaluation, equipping learners with the skills to optimize LLM performance
Focuses on debugging and maintenance techniques for LLMs, enabling learners to address potential issues and ensure smooth operation of LLM-based systems
Explored applications of LLMs, showcasing their versatility and potential for solving real-world problems
Requires prior knowledge in basic machine learning and intermediate Python, making it suitable for learners with some foundational understanding of these areas
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