Amazon SageMaker for ML Application Development
Discover powerful techniques for embedding AI capabilities into applications through Amazon SageMaker's comprehensive machine learning platform.
Discover powerful techniques for embedding AI capabilities into applications through Amazon SageMaker's comprehensive machine learning platform.
This course teaches application developers how to use Amazon SageMaker to simplify machine learning integration into their applications. You'll learn about key machine learning concepts, using Jupyter Notebooks for model training, and publishing models with SageMaker. The curriculum covers SageMaker's built-in algorithms, hyperparameter tuning, and integrating SageMaker endpoints with serverless applications. By the end of the course, you'll be able to build a serverless application that leverages SageMaker for machine learning capabilities, enhancing your skills in this rapidly growing and sought-after field.
Instructors:
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What you'll learn
Learn key problems that Machine Learning can address and solve
Train models using Amazon SageMaker's built-in algorithms and Jupyter Notebooks
Publish and deploy machine learning models using Amazon SageMaker
Integrate published SageMaker endpoints with applications
Understand and apply ML and SageMaker terminology and concepts
Explore hyperparameter tuning for optimizing model performance
Skills you'll gain
This course includes:
PreRecorded video
Weekly quizzes, Final assessment (for verified track)
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
This course focuses on using Amazon SageMaker for machine learning application development. It covers key ML concepts, SageMaker's built-in algorithms, and integration with applications. Topics include using Jupyter Notebooks for model training, hyperparameter tuning, and deploying models. The course also explores serverless application integration and bringing your own models to SageMaker. Through lectures, demonstrations, and hands-on exercises, students learn to effectively use SageMaker in the AWS ecosystem.
Introduction to Machine Learning with SageMaker on AWS
Module 1
Amazon SageMaker Notebooks and SDK
Module 2
Amazon SageMaker Algorithms
Module 3
Amazon SageMaker Algorithms
Module 4
Fee Structure
Instructors
Senior Cloud Technologist
Russ has been in the tech industry since the very early days of the web. After many years in software, Russ made the switch to education and has found the area to be very rewarding. He looks back fondly on the days of dial up internet and under construction icons. When not trying his hardest to keep up with the tech Russ is kept very busy with family chores all over Sydney.
AWS Solutions Architect and AI/ML Expert
Asim Jalis serves as a Senior Solutions Architect at Amazon Web Services, specializing in AI/ML and Analytics solutions. Previously serving as a Senior Technical Trainer at AWS, he holds an MS in Computer Science and brings extensive experience in technical education and cloud architecture. His current work focuses on helping media customers architect and implement AI/ML solutions, particularly in areas of machine learning and analytics. He has contributed significantly to AWS's technical initiatives, including developing solutions for media asset management and data analytics. His expertise spans cloud architecture, machine learning implementation, and enterprise-scale data solutions, making him a valuable resource for AWS customers seeking to leverage advanced technologies.
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Frequently asked questions
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