Comparing SageMaker and Azure ML for MLOps
This course is part of Machine Learning Operations.
This comprehensive course teaches students to build end-to-end machine learning pipelines on leading cloud platforms. The curriculum covers essential topics from data engineering and exploratory analysis to model training and deployment. Students learn to create data repositories, implement ETL pipelines, and develop serverless solutions while gaining hands-on experience with both AWS SageMaker and Azure ML. The course emphasizes practical MLOps skills through real-world projects and prepares participants for AWS and Azure ML certifications.
Instructors:
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What you'll learn
Apply exploratory data analysis techniques to solve data science problems
Build and deploy machine learning solutions using AWS SageMaker
Implement ML pipelines on Azure ML platform
Manage production ML systems using cloud technologies
Optimize model performance and monitoring in cloud environments
Prepare for AWS and Azure ML certifications
Skills you'll gain
This course includes:
30 Hours PreRecorded video
15 quizzes, 9 ungraded labs, multiple readings
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
The course provides comprehensive training in using AWS SageMaker and Azure ML for machine learning operations. Students learn to implement full ML lifecycles, from data engineering to model deployment. The curriculum covers data storage, processing pipelines, exploratory analysis, model training, and production deployment. Through hands-on exercises and real-world scenarios, participants develop practical skills in building and maintaining ML solutions on cloud platforms.
Data Engineering with AWS Technology
Module 1
Exploratory Data Analysis with AWS Technology
Module 2
Modeling with AWS Technology
Module 3
MLOps with AWS Technology
Module 4
Machine Learning Certifications
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
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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