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Updated in [August 18th, 2023]
Skills and Knowledge:
By taking this course, participants will acquire the skills and knowledge necessary to integrate Machine Learning into their applications using Amazon SageMaker. This includes an understanding of Machine Learning and the problems it can help solve, how to use a Jupyter Notebook to train a model based on SageMaker’s built-in algorithms, and how to use SageMaker to publish the validated model. Additionally, participants will gain experience building a serverless application that integrates with the SageMaker published endpoint.
Professional Growth:
This course provides application developers with the knowledge and skills to use Amazon SageMaker to simplify the integration of Machine Learning into their applications. Through lectures, demonstrations, discussions and hands-on exercises, developers will gain an understanding of Machine Learning and the problems it can help solve, as well as how to use a Jupyter Notebook to train a model based on SageMaker’s built-in algorithms and how to use SageMaker to publish the validated model. Completion of the course will enable developers to build a serverless application that integrates with the SageMaker published endpoint. This course contributes to professional growth by providing developers with the skills and knowledge to use Amazon SageMaker to develop applications that incorporate Machine Learning.
Further Education:
Yes, this course is suitable for preparing further education in the field of Machine Learning. It provides an overview of Machine Learning and how to use Amazon SageMaker to integrate it into applications. It also covers topics such as training models with SageMaker's built-in algorithms and building a serverless application that integrates with the SageMaker published endpoint. The course is taught by AWS Training and Certification expert instructors and includes lectures, demonstrations, discussions, and hands-on exercises.