Rust for Large Language Model Operations (LLMOps)
Master LLMOps using Rust programming to integrate, deploy, and manage large language models with frameworks like HuggingFace and AWS infrastructure.
Master LLMOps using Rust programming to integrate, deploy, and manage large language models with frameworks like HuggingFace and AWS infrastructure.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Rust Programming Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Integrate Rust with LLM frameworks and tools
Deploy models on cloud infrastructure
Implement MLOps workflows and pipelines
Build production-ready AI applications
Utilize advanced NLP models and techniques
Skills you'll gain
This course includes:
3.9 Hours PreRecorded video
5 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on implementing Large Language Model Operations using Rust programming language. Students learn to integrate Rust with sophisticated LLM frameworks like HuggingFace Transformers and Candle. The curriculum covers MLOps principles, cloud deployment on AWS, and hands-on experience with state-of-the-art models including StarCoder and Whisper. Topics include DevOps methodologies, model training, and production deployment strategies.
DevOps Concepts for MLOps
Module 1 · 6 Hours to complete
Rust Hugging Face Candle
Module 2 · 4 Hours to complete
Key LLMOps Technologies
Module 3 · 3 Hours to complete
Key Generative AI Technologies
Module 4 · 3 Hours to complete
Fee Structure
Instructors
Executive in Residence and Founder of Pragmatic AI Labs at Duke University
Noah Gift is the founder of Pragmatic AI Labs and serves as an Executive in Residence at Duke University, where he lectures in the Master of Interdisciplinary Data Science (MIDS) program. He specializes in designing and teaching graduate-level courses on machine learning, MLOps, artificial intelligence, and data science, while also consulting on machine learning and cloud architecture for students and faculty. A recognized expert in the field, Gift is a Python Software Foundation Fellow and an AWS Machine Learning Hero, holding multiple AWS certifications, including AWS Certified Solutions Architect and AWS Certified Machine Learning Specialist. He has authored several influential books, such as Practical MLOps, Python for DevOps, and Pragmatic AI, and has published over 100 technical articles across various platforms, including Forbes and O'Reilly. His extensive industry experience includes roles as CTO and Chief Data Scientist for notable companies like Disney Feature Animation, Sony Imageworks, and AT&T, contributing to major films like Avatar and Spider-Man 3. Gift's work has generated millions in revenue through product development on a global scale. He actively consults startups on machine learning and cloud architecture while leading initiatives to enhance data science education.
Adjunct Assistant Professor in the Pratt School of Engineering at Duke University
Alfredo Deza is an Adjunct Assistant Professor at Duke University's Pratt School of Engineering, where he specializes in teaching courses related to machine learning, data engineering, and programming. He has a rich background in software engineering and DevOps, with nearly two decades of experience working with various organizations, including ABC, Caltech, and Disney. Alfredo is also a co-author of several influential books on Python and MLOps, including Python for DevOps and Practical MLOps.On Coursera, Alfredo offers a wide range of courses such as "Advanced Data Engineering," "Introduction to Generative AI," and "MLOps Platforms: Amazon SageMaker and Azure ML." His courses are designed to equip students with essential skills in data engineering and machine learning operations, preparing them for careers in these rapidly evolving fields. Through his teaching and extensive industry experience, Alfredo aims to bridge the gap between theoretical knowledge and practical application in technology.
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