DevOps and MLOps Fundamentals
Master end-to-end ML operations using DevOps, DataOps, and MLOps principles and tools for efficient AI deployment.
Master end-to-end ML operations using DevOps, DataOps, and MLOps principles and tools for efficient AI deployment.
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.
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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
Instructor
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.
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