DevOps and MLOps Fundamentals
This course is part of Machine Learning Operations.
Comprehensive course covering the integration of DevOps, DataOps, and MLOps practices for machine learning solutions. Learn to build and deploy ML applications using modern tools like GitHub Copilot, Gradio, and Hugging Face. Master containerization, cloud deployment, and high-performance computing with Rust for ML applications.
4.5
Instructors:
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What you'll learn
Build interactive ML applications using modern web frameworks
Develop efficient command-line tools for ML applications
Implement high-performance solutions using Rust
Master containerization and cloud deployment
Create integrated DevOps and MLOps pipelines
Optimize ML workflows using modern tools
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This comprehensive course teaches the integration of DevOps, DataOps, and MLOps practices for machine learning operations. Students learn to build end-to-end ML solutions using modern tools and frameworks. The curriculum covers MLOps fundamentals, essential math for data science, pipeline development, and advanced topics like Rust implementation for high-performance computing. Key focus areas include containerization, cloud deployment, and AI-powered development tools.
Introduction to MLOps
Module 1
Essential Math and Data Science
Module 2
Operations Pipelines: DevOps, DataOps, MLOps
Module 3
End to End MLOps and AIOps
Module 4
Rust for MLOps: The Practical Transition from Python to Rust
Module 5
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Machine Learning Operations
Instructor
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.
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