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Ria Cheruvu
This course teaches you how to build impactful, real-world solutions with large language models (LLM) that transform the way we interact with data, with hands-on guidance and key considerations on using these tools effectively and responsibly.
Read more
This course teaches you how to build impactful, real-world solutions with large language models (LLM) that transform the way we interact with data, with hands-on guidance and key considerations on using these tools effectively and responsibly.
Read more
This course teaches you how to build impactful, real-world solutions with large language models (LLM) that transform the way we interact with data, with hands-on guidance and key considerations on using these tools effectively and responsibly.
Large language models (LLMs) are changing the way we can interact with data, creating new interfaces for us to question and explore different forms of data, such as the internet, email, healthcare records, and more via a textual format. The field of LLMs continues to evolve rapidly, and it can be challenging to identify where to get started building solutions with LLMs and taking advantage of this revolutionary technology.
In this course, Build Solutions with Pre-trained LLMs, you’ll gain the ability to implement different pre-trained language models with popular tools and frameworks, including how to customize (fine-tune), deploy, and build solutions using the models.
First, you’ll explore what makes large language models big and efficient, gaining hands-on experience with popular pre-trained LLMs and using them to solve real-world problems.
Then, you'll dive into practical tools for working with pre-trained LLMs, including the HuggingFace, TensorFlow, and PyTorch libraries.
Next, you’ll discover how to fine-tune pre-trained LLMs for specific tasks or domains, gaining the skills to effectively describe techniques for adapting model architectures and implementing fine-tuning.
Finally, you’ll learn about the pitfalls and challenges of working with pre-trained LLMs and how to overcome them.
When you’re finished with this course, you’ll have the skills and knowledge needed to implement pre-trained LLMs effectively, and confidently be able to build solutions using LLMs to solve real-world problems.
Register for this course and see more details by visiting:
OpenCourser.com/course/pyzvm0/build
What's inside
Syllabus
Course Overview
Running Ready-made Pre-trained LLMs
Customize and Deploy LLMs Effectively and Responsibly
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces machine learning, text analysis, data science, and NLP concepts which are essential in the field of technology
Develops foundational skills such as building and deploying LLM models which are highly relevant in the field of data science and machine learning
Offers deep dive into LLM model usage, which can assist in developing models for niche, relevant industries
Hands-on guidance ensures direct application of skills and ability to use LLMs effectively
Covers both theoretical and practical aspects of LLM models, providing learners with a comprehensive view of the technology
Taught by Ria Cheruvu, an experienced instructor in the field of data science and machine learning
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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 Build Solutions with Pre-trained LLMs with these
activities:
Review Course Materials
Show steps
Review syllabuses, assignments, grading breakdowns, and any other materials to understand course requirements and timelines.
Show steps
-
Gather all Course Materials
-
Review the Syllabus
-
Review the Assignment Schedule
-
Review the Grading Rubric
-
Organize Materials for Easy Access
Follow Tutorials on LLM Deployment with TensorFlow or PyTorch
Show steps
Gain practical knowledge by following step-by-step guidance from industry experts.
Browse courses on
TensorFlow
Show steps
-
Identify reputable tutorials from TensorFlow or PyTorch communities.
-
Set up your development environment according to the tutorial instructions.
-
Follow the tutorial steps to deploy an LLM using the chosen framework.
-
Troubleshoot any issues encountered during the deployment process.
Practice Running Ready-made Pre-trained LLMs
Show steps
Reinforce knowledge of running pre-trained LLMs by practicing with different examples.
Show steps
-
Find a dataset of text or code.
-
Choose a pre-trained LLM and run it on the dataset.
-
Evaluate the results and compare them to your expectations.
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Follow a Tutorial on HuggingFace Transformers
Show steps
Enhance practical skills by following a guided tutorial on using HuggingFace Transformers.
Show steps
-
Identify a tutorial that aligns with your learning goals.
-
Set up the necessary environment and tools.
-
Follow the tutorial step-by-step and experiment with the code.
Explore Hugging Face Transformers Library
Show steps
Familiarize yourself with the Hugging Face Transformers library, exploring its capabilities and applications through hands-on tutorials.
Browse courses on
Hugging Face Transformers
Show steps
-
Find appropriate tutorials
-
Install the Hugging Face Transformers library
-
Explore the library's documentation
-
Build a simple LLM-powered application using Hugging Face
Use Hugging Face's Transformers library
Show steps
Gain hands-on experience with Hugging Face's Transformers library.
Show steps
-
Install the Transformers library
-
Load a pre-trained LLM
-
Fine-tune the LLM on a custom dataset
-
Deploy your fine-tuned LLM
Create a Collection of LLM Resources
Show steps
Gather and organize a collection of useful resources related to LLMs, including tutorials, articles, datasets, and tools.
Show steps
-
Identify and gather relevant resources
-
Organize the resources into a coherent collection
-
Create a central location for accessing the collection
-
Share the collection with others
Host a Study Group or Discussion Forum
Show steps
Engage with peers by organizing a study group or discussion forum, fostering collaborative learning and knowledge sharing.
Show steps
-
Find interested peers
-
Choose a regular meeting time and format
-
Establish guidelines and expectations
-
Facilitate discussions and encourage participation
Attend a workshop on LLMs
Show steps
Expand your knowledge of LLMs by attending a workshop led by industry experts.
Show steps
-
Find a workshop that aligns with your interests
-
Register for the workshop
-
Attend the workshop and actively participate
Build a Sentiment Analysis App
Show steps
Demonstrate understanding of customizing LLMs by building a practical application.
Show steps
-
Gather a dataset of labeled text.
-
Fine-tune a pre-trained LLM on the dataset.
-
Create a user interface for the app.
-
Deploy the app and evaluate its performance.
Contribute to Open Source LLM Projects
Show steps
Contribute to open source projects involving LLMs through reporting issues, suggesting improvements, or writing documentation.
Show steps
-
Identify relevant open source LLM projects
-
Review the project's documentation and codebase
-
Choose a way to contribute
-
Submit your contributions to the project
-
Collaborate with other contributors
Practice: Fine-tuning a LLM
Show steps
Enhance your proficiency in fine-tuning LLMs by engaging in hands-on practice, solidifying your understanding of the techniques and their applications.
Browse courses on
Fine-tuning Language Models
Show steps
-
Choose a pre-trained LLM and a dataset relevant to your task
-
Apply different fine-tuning techniques, such as transfer learning or prompt engineering
-
Evaluate the performance of your fine-tuned LLM
-
Iterate and refine your fine-tuning process
Build a custom LLMs solution
Show steps
Solidify your understanding of LLMs by building a custom solution with real-world data.
Show steps
-
Define the problem you want to solve
-
Choose an LLM and fine-tune it on your dataset
-
Deploy your solution and evaluate its performance
Participate in Hackathons or Competitions
Show steps
Engage in time-bound challenges to build solutions using LLMs, fostering teamwork, problem solving, and time management skills.
Show steps
-
Identify relevant hackathons or competitions
-
Form a team or work individually
-
Develop a project idea
-
Build a solution using LLMs
-
Submit the project and participate in the competition
Participate in a LLM hackathon
Show steps
Test your skills and knowledge by participating in a hackathon focused on LLM applications.
Show steps
-
Find a hackathon that aligns with your interests
-
Form a team or work individually
-
Develop a solution within the given timeframe
-
Present your solution to a panel of judges
Write a Tutorial on Fine-tuning LLMs
Show steps
Share knowledge and enhance understanding by creating a comprehensive guide on fine-tuning LLMs.
Show steps
-
Identify the target audience and their level of expertise.
-
Choose a specific aspect of fine-tuning LLMs to focus on.
-
Gather resources and examples to support your explanations.
-
Write clear and concise instructions.
-
Proofread and edit your tutorial.
Tutorial: Deploy LLM solution
Show steps
Gain hands-on experience deploying LLM solutions to enhance your understanding of the practical aspects of building real-world applications.
Show steps
-
Familiarize yourself with a cloud platform like AWS, GCP, or Azure
-
Find a tutorial that aligns with your specific deployment scenario
-
Follow the tutorial steps to deploy your LLM solution
-
Test and evaluate the deployment
Write a Blog Post or Article on LLMs
Show steps
Synthesize your learnings and share insights by writing a blog post or article on LLMs and their applications.
Show steps
-
Choose a topic related to LLMs
-
Research and gather information
-
Organize your thoughts and outline the content
-
Write and edit the blog post or article
-
Publish and promote your content
Project: Build LLM solution for a real-world problem
Show steps
Apply your knowledge and skills to develop a tangible LLM-based solution that addresses a real-world problem, demonstrating your ability to translate theoretical concepts into practical applications.
Show steps
-
Identify a real-world problem that can be solved using LLMs
-
Design and develop an LLM-based solution
-
Train and evaluate your solution using appropriate metrics
-
Document your project and present your findings
Review Course Materials
Show steps
Review syllabuses, assignments, grading breakdowns, and any other materials to understand course requirements and timelines.
Show steps
Show steps
Show steps
- Gather all Course Materials
- Review the Syllabus
- Review the Assignment Schedule
- Review the Grading Rubric
- Organize Materials for Easy Access
Follow Tutorials on LLM Deployment with TensorFlow or PyTorch
Show steps
Gain practical knowledge by following step-by-step guidance from industry experts.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify reputable tutorials from TensorFlow or PyTorch communities.
- Set up your development environment according to the tutorial instructions.
- Follow the tutorial steps to deploy an LLM using the chosen framework.
- Troubleshoot any issues encountered during the deployment process.
Practice Running Ready-made Pre-trained LLMs
Show steps
Reinforce knowledge of running pre-trained LLMs by practicing with different examples.
Show steps
Show steps
Show steps
- Find a dataset of text or code.
- Choose a pre-trained LLM and run it on the dataset.
- Evaluate the results and compare them to your expectations.
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Follow a Tutorial on HuggingFace Transformers
Show steps
Enhance practical skills by following a guided tutorial on using HuggingFace Transformers.
Show steps
Show steps
Show steps
- Identify a tutorial that aligns with your learning goals.
- Set up the necessary environment and tools.
- Follow the tutorial step-by-step and experiment with the code.
Explore Hugging Face Transformers Library
Show steps
Familiarize yourself with the Hugging Face Transformers library, exploring its capabilities and applications through hands-on tutorials.
Browse courses on
Hugging Face Transformers
Show steps
Show steps
Show steps
- Find appropriate tutorials
- Install the Hugging Face Transformers library
- Explore the library's documentation
- Build a simple LLM-powered application using Hugging Face
Use Hugging Face's Transformers library
Show steps
Gain hands-on experience with Hugging Face's Transformers library.
Show steps
Show steps
Show steps
- Install the Transformers library
- Load a pre-trained LLM
- Fine-tune the LLM on a custom dataset
- Deploy your fine-tuned LLM
Create a Collection of LLM Resources
Show steps
Gather and organize a collection of useful resources related to LLMs, including tutorials, articles, datasets, and tools.
Show steps
Show steps
Show steps
- Identify and gather relevant resources
- Organize the resources into a coherent collection
- Create a central location for accessing the collection
- Share the collection with others
Host a Study Group or Discussion Forum
Show steps
Engage with peers by organizing a study group or discussion forum, fostering collaborative learning and knowledge sharing.
Show steps
Show steps
Show steps
- Find interested peers
- Choose a regular meeting time and format
- Establish guidelines and expectations
- Facilitate discussions and encourage participation
Attend a workshop on LLMs
Show steps
Expand your knowledge of LLMs by attending a workshop led by industry experts.
Show steps
Show steps
Show steps
- Find a workshop that aligns with your interests
- Register for the workshop
- Attend the workshop and actively participate
Build a Sentiment Analysis App
Show steps
Demonstrate understanding of customizing LLMs by building a practical application.
Show steps
Show steps
Show steps
- Gather a dataset of labeled text.
- Fine-tune a pre-trained LLM on the dataset.
- Create a user interface for the app.
- Deploy the app and evaluate its performance.
Contribute to Open Source LLM Projects
Show steps
Contribute to open source projects involving LLMs through reporting issues, suggesting improvements, or writing documentation.
Show steps
Show steps
Show steps
- Identify relevant open source LLM projects
- Review the project's documentation and codebase
- Choose a way to contribute
- Submit your contributions to the project
- Collaborate with other contributors
Practice: Fine-tuning a LLM
Show steps
Enhance your proficiency in fine-tuning LLMs by engaging in hands-on practice, solidifying your understanding of the techniques and their applications.
Browse courses on
Fine-tuning Language Models
Show steps
Show steps
Show steps
- Choose a pre-trained LLM and a dataset relevant to your task
- Apply different fine-tuning techniques, such as transfer learning or prompt engineering
- Evaluate the performance of your fine-tuned LLM
- Iterate and refine your fine-tuning process
Build a custom LLMs solution
Show steps
Solidify your understanding of LLMs by building a custom solution with real-world data.
Show steps
Show steps
Show steps
- Define the problem you want to solve
- Choose an LLM and fine-tune it on your dataset
- Deploy your solution and evaluate its performance
Participate in Hackathons or Competitions
Show steps
Engage in time-bound challenges to build solutions using LLMs, fostering teamwork, problem solving, and time management skills.
Show steps
Show steps
Show steps
- Identify relevant hackathons or competitions
- Form a team or work individually
- Develop a project idea
- Build a solution using LLMs
- Submit the project and participate in the competition
Participate in a LLM hackathon
Show steps
Test your skills and knowledge by participating in a hackathon focused on LLM applications.
Show steps
Show steps
Show steps
- Find a hackathon that aligns with your interests
- Form a team or work individually
- Develop a solution within the given timeframe
- Present your solution to a panel of judges
Write a Tutorial on Fine-tuning LLMs
Show steps
Share knowledge and enhance understanding by creating a comprehensive guide on fine-tuning LLMs.
Show steps
Show steps
Show steps
- Identify the target audience and their level of expertise.
- Choose a specific aspect of fine-tuning LLMs to focus on.
- Gather resources and examples to support your explanations.
- Write clear and concise instructions.
- Proofread and edit your tutorial.
Tutorial: Deploy LLM solution
Show steps
Gain hands-on experience deploying LLM solutions to enhance your understanding of the practical aspects of building real-world applications.
Show steps
Show steps
Show steps
- Familiarize yourself with a cloud platform like AWS, GCP, or Azure
- Find a tutorial that aligns with your specific deployment scenario
- Follow the tutorial steps to deploy your LLM solution
- Test and evaluate the deployment
Write a Blog Post or Article on LLMs
Show steps
Synthesize your learnings and share insights by writing a blog post or article on LLMs and their applications.
Show steps
Show steps
Show steps
- Choose a topic related to LLMs
- Research and gather information
- Organize your thoughts and outline the content
- Write and edit the blog post or article
- Publish and promote your content
Project: Build LLM solution for a real-world problem
Show steps
Apply your knowledge and skills to develop a tangible LLM-based solution that addresses a real-world problem, demonstrating your ability to translate theoretical concepts into practical applications.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using LLMs
- Design and develop an LLM-based solution
- Train and evaluate your solution using appropriate metrics
- Document your project and present your findings
Career center
Learners who complete Build Solutions with Pre-trained LLMs will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve real-world problems. This course may be useful for this role as it provides practical experience with LLMs, which are a rapidly growing area of machine learning.
Data Scientist
Data Scientist
A Data Scientist combines programming skills with knowledge of mathematics and statistics to extract insights from data. This course can help build a foundation for this role by providing hands-on experience with LLMs, which are becoming increasingly important in data science.
Content Writer
Content Writer
A Content Writer creates and maintains written content for websites, blogs, and other marketing materials. This course may be useful for this role as it provides hands-on experience with LLMs, which can be used to improve the quality and efficiency of content writing.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs and deploys machine learning models to analyze text data. This course may be useful for this role because it teaches how to utilize pre-trained LLMs to solve real-world problems. The ability to leverage LLMs can enhance the accuracy and efficiency of NLP models.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be useful for this role as it provides hands-on experience with LLMs, which are becoming increasingly important in software development.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation. This course may be useful for this role as it provides hands-on experience with LLMs, which can be used to improve the accuracy and efficiency of technical writing.
Marketing Manager
Marketing Manager
A Marketing Manager develops and executes marketing campaigns to promote products or services. This course may be useful for this role as it provides insights into how LLMs can be used to improve marketing campaigns and reach new customers.
Customer Success Manager
Customer Success Manager
A Customer Success Manager helps customers achieve their goals with a product or service. This course may be useful for this role as it provides insights into how LLMs can be used to improve customer support and satisfaction.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. This course may be useful for this role as it provides insights into how LLMs can be used to improve project management processes and communication.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course may be useful for this role as it provides insights into how LLMs can be used to improve product development and marketing.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. This course may be useful for this role as it provides insights into how LLMs can be used to improve financial analysis and forecasting.
Sales Manager
Sales Manager
A Sales Manager leads and manages a team of sales representatives to achieve sales goals. This course may be useful for this role as it provides insights into how LLMs can be used to improve sales processes and close more deals.
Human Resources Manager
Human Resources Manager
A Human Resources Manager oversees the human resources department of a business. This course may be useful for this role as it provides insights into how LLMs can be used to improve HR processes and employee engagement.
Operations Manager
Operations Manager
An Operations Manager oversees the day-to-day operations of a business. This course may be useful for this role as it provides insights into how LLMs can be used to improve operational efficiency and productivity.
Business Analyst
Business Analyst
A Business Analyst identifies and analyzes business needs and develops solutions to improve business processes. This course may be useful for this role as it provides an understanding of how LLMs can be used to automate and improve business processes.
For more career information including salaries, visit:
OpenCourser.com/course/pyzvm0/build
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
Build Solutions with Pre-trained LLMs.
Serves as a comprehensive guide to deep learning using Python. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks, providing a solid foundation for understanding the underlying principles of LLMs.
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and dialogue systems. It offers a solid foundation for understanding the challenges and techniques involved in working with LLMs.
Serves as a practical guide to deep learning, focusing on the implementation and application of deep learning algorithms. It covers various deep learning architectures and techniques, providing a solid foundation for understanding the technical aspects of LLMs.
Provides hands-on guidance on using PyTorch for NLP tasks. It covers various NLP techniques and models, including transformer models, and offers practical insights into implementing and fine-tuning LLMs using PyTorch.
Provides a practical approach to deep learning using the Fastai and PyTorch libraries. It covers various deep learning architectures and techniques, including transformer models, and offers hands-on examples of implementing and fine-tuning LLMs using these libraries.
Provides practical guidance on deploying and managing LLMs in real-world applications. It covers various considerations, such as infrastructure, security, and monitoring, and offers insights into best practices for operationalizing LLMs.
Save
Explores the potential long-term implications of LLMs and other advanced technologies on humanity. It discusses the potential benefits and risks of LLMs, and offers insights into the ethical and societal challenges that need to be addressed as we move forward with these technologies.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pyzvm0/build
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Time
38 hours
Level
Intermediate
Via
Pluralsight
Instructor
Ria Cheruvu
Language
English
Good to know
Introduces machine learning, text analysis, data science, and NLP concepts which are essential in the field of technology
Develops foundational skills such as building and deploying LLM models which are highly relevant in the field of data science and machine learning
Offers deep dive into LLM model usage, which can assist in developing models for niche, relevant industries
Hands-on guidance ensures direct application of skills and ability to use LLMs effectively
Covers both theoretical and practical aspects of LLM models, providing learners with a comprehensive view of the technology
Taught by Ria Cheruvu, an experienced instructor in the field of data science and machine learning
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