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Matei Zaharia,
Sam Raymond,
Chengyin Eng,
and
Joseph Bradley
This course is aimed at developers, data scientists, and engineers looking to build LLM-centric applications with the latest and most popular frameworks. You will use Hugging Face to solve natural language processing (NLP) problems, leverage LangChain to perform complex, multi-stage tasks, and deep-dive into prompt engineering. You will use data embeddings and vector databases to augment LLM pipelines. Additionally, you will fine-tune LLMs with domain-specific data to improve performance and cost, as well as identify the benefits and drawbacks of proprietary models. You will assess societal, safety, and ethical considerations of using LLMs. Finally, you will learn how to deploy your models at scale, leveraging LLMOps best practices.
Read more
This course is aimed at developers, data scientists, and engineers looking to build LLM-centric applications with the latest and most popular frameworks. You will use Hugging Face to solve natural language processing (NLP) problems, leverage LangChain to perform complex, multi-stage tasks, and deep-dive into prompt engineering. You will use data embeddings and vector databases to augment LLM pipelines. Additionally, you will fine-tune LLMs with domain-specific data to improve performance and cost, as well as identify the benefits and drawbacks of proprietary models. You will assess societal, safety, and ethical considerations of using LLMs. Finally, you will learn how to deploy your models at scale, leveraging LLMOps best practices.
Read more
This course is aimed at developers, data scientists, and engineers looking to build LLM-centric applications with the latest and most popular frameworks. You will use Hugging Face to solve natural language processing (NLP) problems, leverage LangChain to perform complex, multi-stage tasks, and deep-dive into prompt engineering. You will use data embeddings and vector databases to augment LLM pipelines. Additionally, you will fine-tune LLMs with domain-specific data to improve performance and cost, as well as identify the benefits and drawbacks of proprietary models. You will assess societal, safety, and ethical considerations of using LLMs. Finally, you will learn how to deploy your models at scale, leveraging LLMOps best practices.
By the end of this course, you will have built an end-to-end LLM workflow that is ready for production!
Register for this course and see more details by visiting:
OpenCourser.com/course/6n7ovy/large
What's inside
Learning objectives
- How to apply generative ai (genai) / llms to real-world problems in natural language processing (nlp) using popular libraries, such as hugging face and langchain.
- How to add domain knowledge and memory into llm pipelines using embeddings and vector databases.
- Understand the nuances of pre-training, fine-tuning, and prompt engineering, and apply that knowledge to fine-tune a custom chat model
- How to evaluate the efficacy and bias of llms using different methods.
- How to implement llmops and multi-step reasoning best practices for an llm workflow.
- How to apply generative ai (genai) / llms to real-world problems in natural language processing (nlp) using popular libraries, such as hugging face and langchain.
- How to add domain knowledge and memory into llm pipelines using embeddings and vector databases.
- Understand the nuances of pre-training, fine-tuning, and prompt engineering, and apply that knowledge to fine-tune a custom chat model
- How to evaluate the efficacy and bias of llms using different methods.
- How to implement llmops and multi-step reasoning best practices for an llm workflow.
Syllabus
Module 1 - Applications with LLMs
Module 2 - Embeddings, Vector Databases and Search
Module 3 - Multi-stage Reasoning
Module 4 - Fine-tuning and Evaluating LLMs
Read more
Syllabus
Module 1 - Applications with LLMs
Module 2 - Embeddings, Vector Databases and Search
Module 3 - Multi-stage Reasoning
Module 4 - Fine-tuning and Evaluating LLMs
Read more
Module 5 - Society and LLMs: Bias and Safety
Module 6 - LLMOps
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a solid foundation in LLM applications, covering natural language processing (NLP) concepts
Incorporates industry-standard tools like Hugging Face and LangChain, ensuring relevance to real-world use cases
Suitable for individuals seeking to build and deploy LLM-centric applications at scale, including those with experience in engineering and data science
Taught by experienced instructors with notable contributions to the LLM field, including Matei Zaharia
Requires familiarity with LLM principles and some programming experience, which may limit accessibility for beginners
Covers advanced topics like LLMOps and prompt engineering, which may be too specialized for those seeking a general overview of LLMs
Save this course
Save Large Language Models: Application through Production 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 Large Language Models: Application through Production with these
activities:
Review natural language processing (NLP) concepts
Show steps
Begin these quests with a thorough grounding in NLP concepts, bolstering your foundational understanding of the field and preparing you to tackle the course's challenges head-on.
Browse courses on
Natural Language Processing
Show steps
-
Dive into a comprehensive NLP textbook
-
Explore online courses and tutorials on NLP
-
Work through practice problems and exercises
Revise Python Programming Basics
Show steps
Refresher Python will ensure you have the necessary programming skills to work with LLMs.
Browse courses on
Python Programming
Show steps
-
Review core Python concepts such as data types, variables, and control flow.
-
Practice writing simple Python scripts.
Review Fundamentals of Natural Language Processing
Show steps
Reviewing the basics of NLP will provide a solid foundation for building LLM-centric applications.
Browse courses on
Natural Language Processing
Show steps
-
Study the core concepts of NLP, such as tokenization, stemming, and lemmatization.
-
Explore different NLP techniques, including text classification, sentiment analysis, and language generation.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Compile a Glossary of LLM-Related Terms
Show steps
Creating a glossary will help you understand and retain key LLM-related terminology.
Show steps
-
Collect and define essential LLM-related terms.
-
Organize the terms into a comprehensive glossary.
Follow tutorials on fine-tuning LLMs
Show steps
Delve deeper into the art of fine-tuning LLMs by following expert-crafted tutorials. These guided sessions will empower you with practical knowledge and techniques for customizing LLMs to meet specific needs.
Show steps
-
Identify a tutorial that aligns with your interests and skill level
-
Follow the tutorial step-by-step
-
Experiment with different fine-tuning techniques
Participate in Weekly Discussion Forums
Show steps
Engaging in discussions will foster collaboration and enhance your understanding of course concepts.
Show steps
-
Actively participate in weekly discussion forums.
-
Share your insights, ask questions, and engage with your peers.
Practice using Hugging Face for NLP tasks
Show steps
Reinforce your NLP mastery through hands-on practice with Hugging Face. Engage in targeted drills that hone your skills in utilizing this essential tool.
Browse courses on
Hugging Face
Show steps
-
Complete a series of guided tutorials on Hugging Face
-
Work through a variety of NLP tasks using Hugging Face
-
Contribute to open-source projects that leverage Hugging Face
Practice Prompt Engineering Exercises
Show steps
Mastering prompt engineering techniques will significantly enhance your ability to get desired outputs from LLMs.
Browse courses on
Prompt Engineering
Show steps
-
Experiment with different prompt formats, such as natural language, JSON, and code.
-
Fine-tune prompts to achieve specific outcomes and improve LLM performance.
Build an NLP project that leverages LangChain
Show steps
Embark on a practical project that harnesses the power of LangChain to perform complex, multi-stage NLP tasks. This hands-on experience will solidify your understanding of LangChain's capabilities.
Browse courses on
LangChain
Show steps
-
Identify a real-world problem that LangChain can address
-
Design and implement a solution using LangChain
-
Evaluate the performance of your solution
Participate in a hackathon focused on LLMs
Show steps
Put your LLM skills to the test in a fast-paced hackathon environment. Collaborate with like-minded individuals to develop innovative solutions that leverage the power of LLMs, pushing the boundaries of what's possible.
Show steps
-
Find a hackathon that aligns with your interests and skill level
-
Form a team and brainstorm ideas
-
Develop and submit your solution
Build an LLM-Powered Chatbot with Hugging Face
Show steps
Hands-on experience building an LLM chatbot will solidify your understanding of the practical aspects of LLM applications.
Browse courses on
Hugging Face
Show steps
-
Follow a guided tutorial to set up a Hugging Face environment.
-
Train a custom chatbot model using a pre-trained LLM and fine-tuning techniques.
-
Deploy the chatbot and test its performance with different user inputs.
Develop a data visualization tool that showcases LLM bias
Show steps
Craft an interactive data visualization tool that unveils the intricacies of LLM bias. Through this project, you'll gain practical insights into the ethical implications of LLMs and contribute to the ongoing discussion on addressing bias in AI systems.
Browse courses on
LLMs
Show steps
-
Choose a dataset that exhibits LLM bias
-
Design and develop a data visualization tool
-
Share your tool with others and encourage discussions on LLM bias
Assist Fellow Students as a Course Mentor
Show steps
Mentoring others will reinforce your understanding of the course material while also helping your peers.
Show steps
-
Volunteer to be a course mentor.
-
Provide support and guidance to fellow students.
Develop an LLM-Based Product Recommendation System
Show steps
Building a complete LLM-based product recommendation system will challenge you to apply all the concepts learned in the course.
Show steps
-
Gather and pre-process product data, including features, descriptions, and user reviews.
-
Train an LLM on the product data to learn product embeddings.
-
Develop an algorithm to generate personalized product recommendations based on user preferences and context.
-
Evaluate the system's performance using relevant metrics and user feedback.
Review natural language processing (NLP) concepts
Show steps
Begin these quests with a thorough grounding in NLP concepts, bolstering your foundational understanding of the field and preparing you to tackle the course's challenges head-on.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Dive into a comprehensive NLP textbook
- Explore online courses and tutorials on NLP
- Work through practice problems and exercises
Revise Python Programming Basics
Show steps
Refresher Python will ensure you have the necessary programming skills to work with LLMs.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review core Python concepts such as data types, variables, and control flow.
- Practice writing simple Python scripts.
Review Fundamentals of Natural Language Processing
Show steps
Reviewing the basics of NLP will provide a solid foundation for building LLM-centric applications.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Study the core concepts of NLP, such as tokenization, stemming, and lemmatization.
- Explore different NLP techniques, including text classification, sentiment analysis, and language generation.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Compile a Glossary of LLM-Related Terms
Show steps
Creating a glossary will help you understand and retain key LLM-related terminology.
Show steps
Show steps
Show steps
- Collect and define essential LLM-related terms.
- Organize the terms into a comprehensive glossary.
Follow tutorials on fine-tuning LLMs
Show steps
Delve deeper into the art of fine-tuning LLMs by following expert-crafted tutorials. These guided sessions will empower you with practical knowledge and techniques for customizing LLMs to meet specific needs.
Show steps
Show steps
Show steps
- Identify a tutorial that aligns with your interests and skill level
- Follow the tutorial step-by-step
- Experiment with different fine-tuning techniques
Participate in Weekly Discussion Forums
Show steps
Engaging in discussions will foster collaboration and enhance your understanding of course concepts.
Show steps
Show steps
Show steps
- Actively participate in weekly discussion forums.
- Share your insights, ask questions, and engage with your peers.
Practice using Hugging Face for NLP tasks
Show steps
Reinforce your NLP mastery through hands-on practice with Hugging Face. Engage in targeted drills that hone your skills in utilizing this essential tool.
Browse courses on
Hugging Face
Show steps
Show steps
Show steps
- Complete a series of guided tutorials on Hugging Face
- Work through a variety of NLP tasks using Hugging Face
- Contribute to open-source projects that leverage Hugging Face
Practice Prompt Engineering Exercises
Show steps
Mastering prompt engineering techniques will significantly enhance your ability to get desired outputs from LLMs.
Browse courses on
Prompt Engineering
Show steps
Show steps
Show steps
- Experiment with different prompt formats, such as natural language, JSON, and code.
- Fine-tune prompts to achieve specific outcomes and improve LLM performance.
Build an NLP project that leverages LangChain
Show steps
Embark on a practical project that harnesses the power of LangChain to perform complex, multi-stage NLP tasks. This hands-on experience will solidify your understanding of LangChain's capabilities.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Identify a real-world problem that LangChain can address
- Design and implement a solution using LangChain
- Evaluate the performance of your solution
Participate in a hackathon focused on LLMs
Show steps
Put your LLM skills to the test in a fast-paced hackathon environment. Collaborate with like-minded individuals to develop innovative solutions that leverage the power of LLMs, pushing the boundaries of what's possible.
Show steps
Show steps
Show steps
- Find a hackathon that aligns with your interests and skill level
- Form a team and brainstorm ideas
- Develop and submit your solution
Build an LLM-Powered Chatbot with Hugging Face
Show steps
Hands-on experience building an LLM chatbot will solidify your understanding of the practical aspects of LLM applications.
Browse courses on
Hugging Face
Show steps
Show steps
Show steps
- Follow a guided tutorial to set up a Hugging Face environment.
- Train a custom chatbot model using a pre-trained LLM and fine-tuning techniques.
- Deploy the chatbot and test its performance with different user inputs.
Develop a data visualization tool that showcases LLM bias
Show steps
Craft an interactive data visualization tool that unveils the intricacies of LLM bias. Through this project, you'll gain practical insights into the ethical implications of LLMs and contribute to the ongoing discussion on addressing bias in AI systems.
Browse courses on
LLMs
Show steps
Show steps
Show steps
- Choose a dataset that exhibits LLM bias
- Design and develop a data visualization tool
- Share your tool with others and encourage discussions on LLM bias
Assist Fellow Students as a Course Mentor
Show steps
Mentoring others will reinforce your understanding of the course material while also helping your peers.
Show steps
Show steps
Show steps
- Volunteer to be a course mentor.
- Provide support and guidance to fellow students.
Develop an LLM-Based Product Recommendation System
Show steps
Building a complete LLM-based product recommendation system will challenge you to apply all the concepts learned in the course.
Show steps
Show steps
Show steps
- Gather and pre-process product data, including features, descriptions, and user reviews.
- Train an LLM on the product data to learn product embeddings.
- Develop an algorithm to generate personalized product recommendations based on user preferences and context.
- Evaluate the system's performance using relevant metrics and user feedback.
Career center
Learners who complete Large Language Models: Application through Production will develop knowledge and skills
that may be useful to these careers:
NLP Engineer
NLP Engineer
NLP Engineers focus on building and maintaining natural language processing models and applications. This course will directly help build skills for this role, as it will teach about large language model programming, frameworks, and approaches to language processing.
Data Scientist
Data Scientist
Data Scientists use machine learning and programming to solve business and research problems. This course will help build a foundation for working with natural language modeling, embeddings, and more through programming assignments and exercises.
Software Engineer
Software Engineer
Software Engineers create and maintain software applications, including data pipelines and tools that use large language models. This course will help build a foundation for working with natural language modeling through hands-on exercises.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models and applications. This course may be helpful for those specializing in natural language processing or text-based applications, as it covers large language models, pipelines, and techniques for NLP.
Research Scientist
Research Scientist
Research Scientists conduct research in various fields, including natural language processing and machine learning. This course may be helpful for those interested in researching or developing new NLP techniques or applications.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and present data to inform decision making. This course may be helpful for those interested in analyzing unstructured text data, as it covers natural language processing techniques and applications.
Product Manager
Product Manager
Product Managers define and oversee the development of software products, including those that use natural language processing. This course may be helpful for those interested in managing NLP-related products, as it provides an overview of NLP techniques, pipelines, and applications.
Business Analyst
Business Analyst
Business Analysts analyze business needs and develop solutions to meet those needs. This course may be helpful for those interested in analyzing business needs related to natural language processing or text-based applications, as it covers NLP techniques, applications, and pipelines.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for software, products, and services. This course may be helpful for those interested in writing documentation for NLP-related products or services, as it provides an overview of NLP techniques, applications, and pipelines.
Content Creator
Content Creator
Content Creators create and maintain content for various platforms, including websites, social media, and blogs. This course may be helpful for those interested in creating NLP-related content, as it provides an overview of NLP techniques, applications, and pipelines.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns to promote products and services. This course may be helpful for those interested in marketing NLP-related products or services, as it provides an overview of NLP techniques, applications, and pipelines.
Sales Manager
Sales Manager
Sales Managers lead and manage sales teams to achieve sales goals. This course may be helpful for those interested in selling NLP-related products or services, as it provides an overview of NLP techniques, applications, and pipelines.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers achieve success with a company's products and services. This course may be helpful for those interested in helping customers with NLP-related products or services, as it provides an overview of NLP techniques, applications, and pipelines.
Project Manager
Project Manager
Project Managers plan and execute projects to achieve specific goals. This course may be helpful for those interested in managing NLP-related projects, as it provides an overview of NLP techniques, applications, and pipelines.
Consultant
Consultant
Consultants provide advice and expertise to clients on a variety of topics, including NLP. This course may be helpful for those interested in consulting on NLP-related projects, as it provides an overview of NLP techniques, applications, and pipelines.
For more career information including salaries, visit:
OpenCourser.com/course/6n7ovy/large
Reading list
We've selected eight 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
Large Language Models: Application through Production.
Save
A classic textbook on deep learning, providing a comprehensive overview of the field. It offers a deeper understanding of the underlying principles behind LLMs.
A practical guide to NLP using Python. It provides hands-on experience with the tools and techniques used in the course, offering a more applied perspective.
Explores the theory of multi-stage reasoning, which is relevant to the course's coverage of prompt engineering and multi-stage inference.
Provides a comprehensive overview of NLP evaluation, which is essential for assessing LLM performance, making it a valuable reference for the course.
Save
Explores the history and future of AI, providing a broader context for understanding LLMs and their potential impact.
Provides a solid foundation in information retrieval, which key component of LLM pipelines and could serve as background reading for the course.
Provides a practical introduction to deep learning for coders, which could be useful for understanding the technical underpinnings of LLMs.
While not directly related to LLMs, this book could provide a useful introduction to quantum computing, which is an emerging field with potential applications in AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6n7ovy/large
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Effort
4 to 10 hours per week for 6 weeks
Level
Intermediate
Via
edX
Institution
Databricks
Instructors
Matei Zaharia
Sam Raymond
Chengyin Eng
Joseph Bradley
Language
English
Transcript
English
Good to know
Provides a solid foundation in LLM applications, covering natural language processing (NLP) concepts
Incorporates industry-standard tools like Hugging Face and LangChain, ensuring relevance to real-world use cases
Suitable for individuals seeking to build and deploy LLM-centric applications at scale, including those with experience in engineering and data science
Taught by experienced instructors with notable contributions to the LLM field, including Matei Zaharia
Requires familiarity with LLM principles and some programming experience, which may limit accessibility for beginners
Covers advanced topics like LLMOps and prompt engineering, which may be too specialized for those seeking a general overview of LLMs
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