Build Solutions with Pre-trained LLMs from Pluralsight

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.

What's inside

Syllabus

Course Overview

Running Ready-made Pre-trained LLMs

Customize and Deploy LLMs Effectively and Responsibly

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

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

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Review syllabuses, assignments, grading breakdowns, and any other materials to understand course requirements and timelines.

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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

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Gain practical knowledge by following step-by-step guidance from industry experts.

Browse courses on TensorFlow

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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

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Reinforce knowledge of running pre-trained LLMs by practicing with different examples.

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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

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Follow a Tutorial on HuggingFace Transformers

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Enhance practical skills by following a guided tutorial on using HuggingFace Transformers.

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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

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Familiarize yourself with the Hugging Face Transformers library, exploring its capabilities and applications through hands-on tutorials.

Browse courses on Hugging Face Transformers

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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

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Gain hands-on experience with Hugging Face's Transformers library.

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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

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Gather and organize a collection of useful resources related to LLMs, including tutorials, articles, datasets, and tools.

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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

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Engage with peers by organizing a study group or discussion forum, fostering collaborative learning and knowledge sharing.

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Find interested peers
Choose a regular meeting time and format
Establish guidelines and expectations
Facilitate discussions and encourage participation

Attend a workshop on LLMs

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Expand your knowledge of LLMs by attending a workshop led by industry experts.

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Find a workshop that aligns with your interests
Register for the workshop
Attend the workshop and actively participate

Build a Sentiment Analysis App

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Demonstrate understanding of customizing LLMs by building a practical application.

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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

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Contribute to open source projects involving LLMs through reporting issues, suggesting improvements, or writing documentation.

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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

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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

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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

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Solidify your understanding of LLMs by building a custom solution with real-world data.

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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

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Engage in time-bound challenges to build solutions using LLMs, fostering teamwork, problem solving, and time management skills.

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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

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Test your skills and knowledge by participating in a hackathon focused on LLM applications.

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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

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Share knowledge and enhance understanding by creating a comprehensive guide on fine-tuning LLMs.

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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

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Gain hands-on experience deploying LLM solutions to enhance your understanding of the practical aspects of building real-world applications.

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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

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Synthesize your learnings and share insights by writing a blog post or article on LLMs and their applications.

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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

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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.

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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

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.

See salaries and explore the career path for Machine Learning Engineer

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.

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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.

See salaries and explore the career path for Content Writer

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.

See salaries and explore the career path for Natural Language Processing 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.

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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.

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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.

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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.

See salaries and explore the career path for Customer Success 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.

See salaries and explore the career path for Project 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.

See salaries and explore the career path for Product Manager

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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Business Analyst