MLOps Platforms: Amazon SageMaker and Azure ML
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
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.
20 Courses
A Technology Educator and Former Olympic Athlete Pioneering AI Innovation
Alfredo Deza embodies a unique combination of athletic excellence and technological expertise, transitioning from a distinguished career as Peru's first World Junior Champion in high jump and 2004 Olympian to becoming a leading voice in technology education and development. Currently serving as a Principal Cloud Advocate at Microsoft and Adjunct Assistant Professor at Duke University's Pratt School of Engineering, Deza has built an impressive career spanning nearly two decades in software engineering and education. His academic contributions extend through guest lectures at prestigious institutions including Oxford University, Georgia Tech, and Carnegie Mellon University, where he shares expertise in machine learning, cloud computing, and programming languages. As an accomplished author, he has co-authored several influential books with O'Reilly Media, including "Practical MLOps" and "Python for DevOps," while developing comprehensive courses on Coursera covering topics from large language models to Rust programming. His teaching portfolio at Duke includes graduate-level courses in machine learning operations and Python programming, reflecting his commitment to making complex technical concepts accessible. Deza's expertise encompasses a broad spectrum of technologies, including Azure, MLOps, DevOps, Python, Rust, and Databricks, which he leverages to bridge the gap between academic theory and industry practice. His unique perspective, shaped by his background as an Olympic athlete, influences his approach to teaching and technology, emphasizing the importance of continuous learning and knowledge sharing in the rapidly evolving field of artificial intelligence and cloud computing.
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