Open Source LLM Operations
Master open source Large Language Models through hands-on training in architecture, fine-tuning, and deployment.
Master open source Large Language Models through hands-on training in architecture, fine-tuning, and deployment.
This comprehensive course dives deep into open source Large Language Models (LLMs), focusing on practical implementation and operations. Students will explore cutting-edge architectures like Transformers, gain hands-on experience with model fine-tuning using SkyPilot, and master efficient deployment using LoRAX and vLLM. The curriculum covers running pre-trained models, understanding LLM architectures, and implementing advanced deployment strategies, all while using state-of-the-art open source tools.
Instructors:
English
English
Powered by
What you'll learn
Deploy and run local large language models effectively
Master model fine-tuning techniques with SkyPilot
Implement efficient model serving using LoRAX and vLLM
Develop practical solutions using open source AI tools
Understand and work with advanced LLM architectures
Create scalable training workflows across cloud providers
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This intensive course provides a thorough exploration of open source Large Language Models and their practical applications. Starting with fundamental concepts, students learn to access and utilize pre-trained models through libraries like HuggingFace Transformers. The curriculum progresses through local LLM deployment, model fine-tuning, and advanced topics like GPU-accelerated MLOps workflows. Special emphasis is placed on hands-on experience with tools like LLamaFile, Whisper.cpp, and SkyPilot for building and deploying AI solutions.
Getting Started with Open Source Ecosystem
Module 1
Using Local LLMs from LLamaFile to Whisper.cpp
Module 2
Applied Projects
Module 3
Recap and Final Challenges
Module 4
Fee Structure
Instructors
30 Courses
Pioneering Tech Leader & AI Educator Shaping the Future of Machine Learning
Noah Gift is a distinguished technology leader and founder of Pragmatic AI Labs with a remarkable 30-year career spanning film, TV, telecom, social networks, startups, and big data, currently serving as an Executive-in-Residence at Duke University. As an AWS ML Hero and Python Software Foundation Fellow, he has authored best-selling books through O'Reilly and Pearson on DevOps, MLOps, data engineering, and cloud computing that are widely adopted by major universities. His expertise in MLOps, data engineering, cloud architecture, and Rust programming has led him to teach thousands of students across prestigious institutions including Duke, Northwestern, UC Berkeley, University of San Francisco, and UC Davis, while also collaborating with Caltech and JPL on automated IT systems. Gift's impact extends beyond academia through his work as a startup CTO, his role in developing scalable distributed systems, and his contributions as a keynote speaker at conferences focused on cloud development and ethical AI use, holding certifications from AWS, Google, and Microsoft, and having published over 100 technical works while conducting workshops for organizations like NASA, PayPal, and PyCon.
20 Courses
A Technology Educator and Former Olympic Athlete Pioneering AI Innovation
Alfredo Deza embodies a unique combination of athletic excellence and technological expertise, transitioning from a distinguished career as Peru's first World Junior Champion in high jump and 2004 Olympian to becoming a leading voice in technology education and development. Currently serving as a Principal Cloud Advocate at Microsoft and Adjunct Assistant Professor at Duke University's Pratt School of Engineering, Deza has built an impressive career spanning nearly two decades in software engineering and education. His academic contributions extend through guest lectures at prestigious institutions including Oxford University, Georgia Tech, and Carnegie Mellon University, where he shares expertise in machine learning, cloud computing, and programming languages. As an accomplished author, he has co-authored several influential books with O'Reilly Media, including "Practical MLOps" and "Python for DevOps," while developing comprehensive courses on Coursera covering topics from large language models to Rust programming. His teaching portfolio at Duke includes graduate-level courses in machine learning operations and Python programming, reflecting his commitment to making complex technical concepts accessible. Deza's expertise encompasses a broad spectrum of technologies, including Azure, MLOps, DevOps, Python, Rust, and Databricks, which he leverages to bridge the gap between academic theory and industry practice. His unique perspective, shaped by his background as an Olympic athlete, influences his approach to teaching and technology, emphasizing the importance of continuous learning and knowledge sharing in the rapidly evolving field of artificial intelligence and cloud computing.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.