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Large Language Models
Application through Production
Matei Zaharia,
Sam Raymond,
Chengyin Eng,
and
Joseph Bradley
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
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Activities
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Large Language Models: Application through Production.
These are activities you can do either before, during, or after a course.
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
Session
December 1, 2023
- May 31, 2024
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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