DevOps, DataOps, MLOps
Master modern operational frameworks for machine learning deployment, from continuous integration to GPU-accelerated solutions using Python and Rust.
Master modern operational frameworks for machine learning deployment, from continuous integration to GPU-accelerated solutions using Python and Rust.
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 MLOps Machine Learning Operations 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.
4.2
(105 ratings)
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Build MLOps and DevOps pipelines
Implement continuous integration workflows
Deploy ML models in production
Develop GPU-accelerated solutions
Master cloud-native ML architecture
Skills you'll gain
This course includes:
18.7 Hours PreRecorded video
13 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course covers advanced MLOps practices including continuous integration, model deployment, and operational pipelines. Students learn to build end-to-end solutions using modern tools like GitHub Copilot, work with pre-trained models, and transition from Python to Rust for high-performance ML applications.
Introduction to MLOps
Module 1 · 7 Hours to complete
Essential Math and Data Science
Module 2 · 8 Hours to complete
Operations Pipelines
Module 3 · 9 Hours to complete
End to End MLOps and AIOps
Module 4 · 10 Hours to complete
Rust for MLOps
Module 5 · 10 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 at Duke University
Dr. Alfredo Deza is an Adjunct Assistant Professor in the Pratt School of Engineering at Duke University, where he teaches courses on machine learning, programming, and data engineering. He has been involved in academia for several years, focusing on innovative teaching methods and practical applications of technology. Dr. Deza co-authored the book Practical MLOps and has published several other works related to Python and machine learning. His teaching includes courses such as Python Bootcamp and advanced data engineering topics, and he actively develops online courses available on platforms like Coursera. In addition to his academic role, Dr. Deza works in developer relations at Microsoft, leveraging his extensive experience in software engineering and cloud computing to enhance educational content and support for students and faculty. He collaborates with various universities worldwide, including Georgia Tech and Carnegie Mellon University, to promote knowledge sharing in the field of technology and data science.
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4.2 course rating
105 ratings
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