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Noah Gift
and
Alfredo Deza
Learn the fundamentals of large language models (LLMs) and put them into practice by deploying your own solutions based on open source models. By the end of this course, you will be able to leverage state-of-the-art open source LLMs to create AI applications using a code-first approach.
Read more
Learn the fundamentals of large language models (LLMs) and put them into practice by deploying your own solutions based on open source models. By the end of this course, you will be able to leverage state-of-the-art open source LLMs to create AI applications using a code-first approach.
Read more
Learn the fundamentals of large language models (LLMs) and put them into practice by deploying your own solutions based on open source models. By the end of this course, you will be able to leverage state-of-the-art open source LLMs to create AI applications using a code-first approach.
You will start by gaining an in-depth understanding of how LLMs work, including model architectures like transformers and advancements like sparse expert models. Hands-on labs will walk you through launching cloud GPU instances and running pre-trained models like Code Llama, Mistral, and stable diffusion.
The highlight of the course is a guided project where you will fine-tune a model like LLaMA or Mistral on a dataset of your choice. You will use SkyPilot to easily scale model training on low-cost spot instances across cloud providers. Finally, you will containerize your model for efficient deployment using model servers like LoRAX and vLLM.
By the end of the course, you will have first-hand experience leveraging open source LLMs to build AI solutions. The skills you gain will enable you to further advance your career in AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/str85f/open
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What's inside
Syllabus
Getting Started with Open Source Ecosystem
This week, you will learn how to leverage pre-trained natural language processing models to build NLP applications. We will explore popular open source models like BERT. You will learn how to access these models using libraries like HuggingFace Transformers and use them for tasks like text classification, question answering, and text generation. A key skill will be using large language models to synthetically augment datasets. By feeding the model examples and extracting the text it generates, you can create more training data. Through hands-on exercises, you will build basic NLP pipelines in Python that use pre-trained models to perform tasks like sentiment analysis. By the end of the week, you'll have practical experience using state-of-the-art NLP techniques to create capable language applications.
Read more
Syllabus
Getting Started with Open Source Ecosystem
This week, you will learn how to leverage pre-trained natural language processing models to build NLP applications. We will explore popular open source models like BERT. You will learn how to access these models using libraries like HuggingFace Transformers and use them for tasks like text classification, question answering, and text generation. A key skill will be using large language models to synthetically augment datasets. By feeding the model examples and extracting the text it generates, you can create more training data. Through hands-on exercises, you will build basic NLP pipelines in Python that use pre-trained models to perform tasks like sentiment analysis. By the end of the week, you'll have practical experience using state-of-the-art NLP techniques to create capable language applications.
Read more
Using Local LLMs from LLamafile to Whisper.cpp
This week, you run language models locally. Keep data private. Avoid latency and fees. Use Mixtral model and llamafile.
Applied Projects
This week you will use models in the browser with Transformers.js and ONNX. You will gain experience on porting models to the ONNX runtime and experience how to put them on the browser. You will also use the Cosmopolitan project to build a phrase generator that is easily portable on different systems.
Recap and Final Challenges
This week you will focus on completing several external labs and hands-on examples that will allow you to feel comfortable running local LLMs, connect to them with APIs using Python as well as building solutions with the Rust programming language
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed with hands-on labs, extensive exercises, and a guided project that allows students to leverage, fine-tune, and deploy open source large language models
Provides a mix of theory, practical exercises, and hands-on labs to ensure students can grasp the concepts and apply them effectively
Emphasizes the use of state-of-the-art open source large language models, which are highly relevant to the field of artificial intelligence
Taught by instructors who are experts in the field of large language models
Provides exposure to various cloud platforms, including AWS and GCP
Covers topics such as model architecture, training techniques, and deployment strategies for large language models, providing a comprehensive understanding
Save this course
Save Open Source LLMOps Solutions 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 Open Source LLMOps Solutions with these
activities:
Organize Course Materials
Show steps
Collect and organize notes, assignments, and other learning materials from the course to facilitate efficient review and improve retention.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Download and arrange lecture slides, notes, and assignments.
-
Review and summarize key concepts from each module.
Review 'Deep Learning with Python'
Show steps
Review a foundational text on deep learning, which provides a solid understanding of the underlying concepts that underpin LLMs.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read selected chapters related to deep learning fundamentals.
-
Summarize key concepts and techniques.
Start an Open Source Project
Show steps
Contribute to an open source project related to LLMs, enabling hands-on experience in a collaborative environment.
Show steps
-
Find an open source LLM project that aligns with your interests.
-
Identify an area where you can contribute.
-
Submit a pull request with your contribution.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Put LLMs in Practice
Show steps
Practice using LLMs to complete various tasks, such as text generation, translation, and question answering, to solidify understanding.
Show steps
-
Set up a cloud GPU instance and run a pre-trained LLM.
-
Fine-tune a model on a specific dataset.
-
Deploy a model as a REST API.
Practice with Prompt Engineering
Show steps
Experiment with different prompts and techniques to optimize LLM performance, enhancing the ability to effectively communicate with and guide the models.
Show steps
-
Learn best practices for crafting effective prompts.
-
Experiment with different prompt variations.
-
Evaluate the impact of different prompts on model output.
Explore Advanced Transformers
Show steps
Follow tutorials to delve deeper into transformer architectures, such as self-attention and encoder-decoder models, to enhance understanding of LLM functionality.
Browse courses on
Transformers
Show steps
-
Understand the architecture and training process of Transformer models.
-
Implement a Transformer model from scratch.
-
Apply Transformers to a real-world NLP task.
Participate in an LLM Challenge
Show steps
Engage in a competition or challenge that encourages the application of LLM skills, fostering a competitive and growth-oriented mindset.
Show steps
-
Identify an LLM competition or challenge that aligns with your interests.
-
Form a team or work individually.
-
Develop a solution using LLMs.
-
Submit your solution and compete for recognition.
Build an LLM-Powered App
Show steps
Design and develop an application that leverages LLMs to solve a specific problem, reinforcing practical implementation skills.
Show steps
-
Identify a problem that can be solved using an LLM.
-
Choose an appropriate LLM and fine-tune it on a relevant dataset.
-
Develop a user interface for the application.
-
Deploy the application and test its functionality.
Organize Course Materials
Show steps
Collect and organize notes, assignments, and other learning materials from the course to facilitate efficient review and improve retention.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Download and arrange lecture slides, notes, and assignments.
- Review and summarize key concepts from each module.
Review 'Deep Learning with Python'
Show steps
Review a foundational text on deep learning, which provides a solid understanding of the underlying concepts that underpin LLMs.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters related to deep learning fundamentals.
- Summarize key concepts and techniques.
Start an Open Source Project
Show steps
Contribute to an open source project related to LLMs, enabling hands-on experience in a collaborative environment.
Show steps
Show steps
Show steps
- Find an open source LLM project that aligns with your interests.
- Identify an area where you can contribute.
- Submit a pull request with your contribution.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Put LLMs in Practice
Show steps
Practice using LLMs to complete various tasks, such as text generation, translation, and question answering, to solidify understanding.
Show steps
Show steps
Show steps
- Set up a cloud GPU instance and run a pre-trained LLM.
- Fine-tune a model on a specific dataset.
- Deploy a model as a REST API.
Practice with Prompt Engineering
Show steps
Experiment with different prompts and techniques to optimize LLM performance, enhancing the ability to effectively communicate with and guide the models.
Show steps
Show steps
Show steps
- Learn best practices for crafting effective prompts.
- Experiment with different prompt variations.
- Evaluate the impact of different prompts on model output.
Explore Advanced Transformers
Show steps
Follow tutorials to delve deeper into transformer architectures, such as self-attention and encoder-decoder models, to enhance understanding of LLM functionality.
Browse courses on
Transformers
Show steps
Show steps
Show steps
- Understand the architecture and training process of Transformer models.
- Implement a Transformer model from scratch.
- Apply Transformers to a real-world NLP task.
Participate in an LLM Challenge
Show steps
Engage in a competition or challenge that encourages the application of LLM skills, fostering a competitive and growth-oriented mindset.
Show steps
Show steps
Show steps
- Identify an LLM competition or challenge that aligns with your interests.
- Form a team or work individually.
- Develop a solution using LLMs.
- Submit your solution and compete for recognition.
Build an LLM-Powered App
Show steps
Design and develop an application that leverages LLMs to solve a specific problem, reinforcing practical implementation skills.
Show steps
Show steps
Show steps
- Identify a problem that can be solved using an LLM.
- Choose an appropriate LLM and fine-tune it on a relevant dataset.
- Develop a user interface for the application.
- Deploy the application and test its functionality.
Career center
Learners who complete Open Source LLMOps Solutions will develop knowledge and skills
that may be useful to these careers:
NLP Scientist
NLP Scientist
An NLP Scientist researches and develops natural language processing (NLP) technologies. This course in Open Source LLMOps Solutions is highly relevant for an NLP Scientist, as it provides a deep dive into the latest advancements in LLMs and their applications. Hands-on experience with fine-tuning and deploying LLMs will provide a competitive edge in this field.
NLP Engineer
NLP Engineer
A NLP Engineer focuses on developing and implementing natural language processing (NLP) solutions. This course delves into NLP models and their applications, providing a solid base for a NLP Engineer. You will learn how to access and use pre-trained NLP models for tasks like text classification, question answering, and text generation. This course is a valuable stepping stone towards a successful career in NLP engineering.
AI Researcher
AI Researcher
An AI Researcher explores and develops new methods and applications of artificial intelligence (AI). The Open Source LLMOps Solutions course provides a comprehensive overview of the fundamentals of LLMs and their practical applications. This knowledge is crucial for AI Researchers to stay at the forefront of AI advancements. The hands-on projects in this course, such as fine-tuning a model on a dataset of your choice, will provide valuable experience for an AI Researcher.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and maintains machine learning models to solve business problems. The course in Open Source LLMOps Solutions is an excellent starting point in this field. The course will help build a strong foundation in ML model development, deployment, and optimization. Hands-on experience with open source LLMs is highly valued by employers in this field.
AI Engineer
AI Engineer
An AI Engineer designs, builds, and maintains AI systems. This course, Open Source LLMOps Solutions, can help build a foundation for a career as an AI Engineer, as it provides a comprehensive overview of LLMs and their practical applications in various domains. The hands-on experience in deploying and optimizing models will be highly valuable in this role.
AI Product Manager
AI Product Manager
An AI Product Manager leads the development and launch of AI products. This course, Open Source LLMOps Solutions, can help build a foundation for an AI Product Manager, as it provides a deep understanding of LLMs and their potential applications. The hands-on experience in deploying and optimizing models will be particularly valuable in this role.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to provide insights and inform decision-making. This course in Open Source LLMOps Solutions can help build a foundation for a Data Analyst, as it provides hands-on experience with using LLMs to augment datasets and improve data analysis accuracy. This skillset is increasingly important in the field of data analytics.
Machine Learning Scientist
Machine Learning Scientist
A Machine Learning Scientist researches and develops machine learning algorithms and models. This course in Open Source LLMOps Solutions may be useful for a Machine Learning Scientist who wants to specialize in LLMs. The course provides a comprehensive overview of LLM architectures, training techniques, and practical applications. The hands-on projects will provide valuable experience in developing and deploying ML models based on LLMs.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and infrastructure. This course in Open Source LLMOps Solutions may be useful for a Data Engineer who wants to specialize in AI/ML data engineering. The course provides hands-on experience with using LLMs to augment datasets and improve data quality, which is a critical skill for building and maintaining high-quality AI/ML models.
Data Scientist
Data Scientist
A Data Scientist collects and analyzes data to deliver valuable insights and actionable recommendations to organizations. This course, Open Source LLMOps Solutions, can help build a foundation for a career as a Data Scientist as LLMs are increasingly used to gain new insights from data. Utilizing the models discussed in this course can help you automate data analysis, uncover hidden patterns and relationships, and make more accurate predictions.
Consultant
Consultant
A Consultant provides expert advice and guidance to clients on a variety of business issues. This course in Open Source LLMOps Solutions may be useful for a Consultant who wants to specialize in AI/ML consulting. The course provides a comprehensive overview of LLMs and their practical applications in various industries, which will enable the consultant to provide informed advice to clients on how to leverage LLMs to improve their business operations.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst develops and applies mathematical and statistical models to financial data. This course, Open Source LLMOps Solutions, may be useful for a Quantitative Analyst who wants to specialize in AI/ML-driven financial modeling. The course provides a comprehensive overview of LLMs and their applications in finance, such as fraud detection and risk assessment.
Product Designer
Product Designer
A Product Designer designs and develops digital products and services. This course in Open Source LLMOps Solutions may be useful for a Product Designer who wants to specialize in AI/ML-driven product design. The course provides a comprehensive overview of LLMs and their applications in product design, such as personalized recommendations and chatbots.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course in Open Source LLMOps Solutions may be useful for a Software Engineer interested in specializing in AI or ML-driven software development. The emphasis on deploying solutions based on LLMs will provide valuable knowledge for building AI-powered software applications.
Business Analyst
Business Analyst
A Business Analyst analyzes business needs and develops solutions to improve efficiency and productivity. This course, Open Source LLMOps Solutions, may be useful for a Business Analyst who wants to specialize in AI-driven solutions. The course provides a comprehensive overview of LLMs and their practical applications in various business domains.
For more career information including salaries, visit:
OpenCourser.com/course/str85f/open
Reading list
We've selected ten 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
Open Source LLMOps Solutions.
Provides a comprehensive guide to pattern recognition and machine learning, including the theory behind pattern recognition and machine learning algorithms, how to build pattern recognition and machine learning models, and how to use pattern recognition and machine learning for a variety of applications.
Provides a comprehensive guide to machine learning from a probabilistic perspective, including the theory behind machine learning algorithms, how to build machine learning models, and how to use machine learning for a variety of applications.
Save
Provides a comprehensive guide to deep learning, including the theory behind deep learning algorithms, how to train and evaluate deep learning models, and how to use deep learning for a variety of applications.
Provides a comprehensive guide to speech and language processing, including the theory behind speech and language processing algorithms, how to build speech and language processing systems, and how to use speech and language processing for a variety of applications.
Provides a comprehensive guide to reinforcement learning, including the theory behind reinforcement learning algorithms, how to train and evaluate reinforcement learning models, and how to use reinforcement learning for a variety of applications.
Provides a comprehensive guide to probabilistic graphical models, including the theory behind probabilistic graphical models, how to build probabilistic graphical models, and how to use probabilistic graphical models for a variety of applications.
Provides a comprehensive guide to information theory, inference, and learning algorithms, including the theory behind information theory, inference, and learning algorithms, how to build information theory, inference, and learning algorithms, and how to use information theory, inference, and learning algorithms for a variety of applications.
Provides a comprehensive guide to statistical learning, including the theory behind statistical learning algorithms, how to build statistical learning models, and how to use statistical learning for a variety of applications.
Provides a comprehensive guide to using Python for NLP tasks, including how to use Python libraries for NLP, how to preprocess text data, and how to build NLP models.
Save
Provides a comprehensive guide to computer vision, including the theory behind computer vision algorithms, how to build computer vision systems, and how to use computer vision for a variety of applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/str85f/open
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Effort
4 weeks of study, 5 hours/week
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
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 (viewing)
- Operationalizing LLMs on Azure
- GenAI and LLMs on AWS
- Advanced Data Engineering
Good to know
Designed with hands-on labs, extensive exercises, and a guided project that allows students to leverage, fine-tune, and deploy open source large language models
Provides a mix of theory, practical exercises, and hands-on labs to ensure students can grasp the concepts and apply them effectively
Emphasizes the use of state-of-the-art open source large language models, which are highly relevant to the field of artificial intelligence
Taught by instructors who are experts in the field of large language models
Provides exposure to various cloud platforms, including AWS and GCP
Covers topics such as model architecture, training techniques, and deployment strategies for large language models, providing a comprehensive understanding
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