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The Amazon SageMaker Studio for Data Scientists course is designed to equip learners with a comprehensive understanding of AWS SageMaker Studio, a machine learning (ML) integrated development environment (IDE). Learners will explore the setup, navigation, and functionalities of Amazon SageMaker Studio, diving into data processing techniques to ensure ML-readiness and bias detection. The course covers model development, including tuning, evaluation, and debugging, using AWS SageMaker Studio. Deployment and inference modules teach how to effectively manage models and automate ML workflows. Monitoring lessons focus on maintaining model quality and detecting drifts. Finally, the course provides insights into resource management and updates, ensuring learners can efficiently operate within SageMaker Studio. This curriculum caters to data scientists looking to harness the full potential of SageMaker Studio for end-to-end ML solutions.
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Classroom Training price is on request
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
To ensure that participants are prepared for the Amazon SageMaker Studio for Data Scientists course and can fully benefit from the training, the following are the minimum required prerequisites:
These prerequisites are designed to ensure a solid foundation upon which the course material can build, enabling students to grasp the more advanced concepts and practical applications taught in the Amazon SageMaker Studio for Data Scientists course.
The Amazon SageMaker Studio for Data Scientists course offers comprehensive training in ML model development, deployment, and management on AWS.
Target Audience:
Introduction: This course equips data scientists with the skills to utilize Amazon SageMaker Studio for end-to-end machine learning, from setup and data processing to deployment and monitoring.
Learning Objectives and Outcomes: