Machine Learning Engineering and MLOps in the Cloud
This course is part of Cloud Computing Basics Explained.
This comprehensive course explores machine learning engineering principles and MLOps practices for building scalable intelligent systems. Students learn to develop ML applications using software engineering best practices and continuous delivery pipelines. The curriculum covers AutoML technologies, cloud-based solutions, edge machine learning, and AI APIs. Through hands-on experience with tools like Ludwig and Cloud AutoML, participants gain practical skills in implementing and managing ML systems at scale.
Instructors:
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What you'll learn
Implement machine learning engineering best practices
Build and deploy ML applications using continuous delivery
Utilize AutoML for efficient model development
Develop edge machine learning solutions
Integrate AI APIs in applications
Implement MLOps strategies for production systems
Skills you'll gain
This course includes:
PreRecorded video
Quizzes, Discussion Prompts, Ungraded Labs
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 3 modules in this course
The course provides a comprehensive introduction to machine learning engineering and MLOps. Students learn about ML microservices, continuous delivery pipelines, and AutoML solutions. The curriculum covers both open-source and cloud-based tools, edge machine learning implementations, and AI API integration. Practical aspects include working with Flask ML applications, cloud AutoML platforms, and implementing MLOps strategies.
Getting Started with Machine Learning Engineering
Module 1
Using AutoML
Module 2
Emerging Topics in Machine Learning
Module 3
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: Cloud Computing Basics Explained
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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Frequently asked questions
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