AWS: Amazon SageMaker: Simplifying Machine Learning Application Development
|
Learn to integrate Machine Learning into your apps with training from AWS experts–and without a data science background.
About this course
Machine learning is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market.
This course will teach you, an application developer, how to use Amazon SageMaker to simplify the integration of Machine Learning into your applications. Key topics include: an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMaker’s built-in algorithms and, using SageMaker to publish the validated model. You will finish the class by building a serverless application that integrates with the SageMaker published endpoint.
Learn from AWS Training and Certification expert instructors through lectures, demonstrations, discussions and hands-on exercises* as we explore this complex topic from the lens of the application developer.
*Note that there may be a cost associated with some exercises. If you do not wish to incur additional expenses, you may view demonstrations instead.
At a Glance:
Institution: AWS
Subject: Computer Science
Level: Intermediate
Prerequisites:
Prior application development experience
Experience with the AWS Console
Recommended: AWS Developer Professional Series (Building on AWS, Deploying on AWS, Optimizing on AWS)
Language: English
Video Transcript: English
Associated skills:Jupyter Notebook, Application Development, Machine Learning, Data Science, Amazon Web Services, Integration, Serverless Computing, Demonstration Skills, Lecturing, AWS SageMaker, Algorithms
What You’ll Learn:
About this course
Machine learning is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market.
This course will teach you, an application developer, how to use Amazon SageMaker to simplify the integration of Machine Learning into your applications. Key topics include: an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMaker’s built-in algorithms and, using SageMaker to publish the validated model. You will finish the class by building a serverless application that integrates with the SageMaker published endpoint.
Learn from AWS Training and Certification expert instructors through lectures, demonstrations, discussions and hands-on exercises* as we explore this complex topic from the lens of the application developer.
*Note that there may be a cost associated with some exercises. If you do not wish to incur additional expenses, you may view demonstrations instead.
User Reviews
Be the first to review “AWS: Amazon SageMaker: Simplifying Machine Learning Application Development”
Related Products
GTx: Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues
Work with the principles of data storage in Arrays, ArrayLists & LinkedList nodes. Understand their operations and performance with visualizations. Implement low-level linear, linked data structures with recursive methods, and explore their edge cases. Extend these structures to the Abstract Data Types, Stacks, Queues and Deques.
GTx: Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps
Become familiar with nonlinear and hierarchical data structures. Study various tree structures: Binary Trees, BSTs and Heaps. Understand tree operations and algorithms. Learn and implement HashMaps that utilize key-value pairs to store data. Explore probabilistic data structures like SkipLists. Course tools help visualize the structures and performance.
UCSanDiegoX: Machine Learning Fundamentals
Understand machine learning's role in data-driven modeling, prediction, and decision-making.
There are no reviews yet.