Amazon SageMaker Studio for Data Scientists Course Overview

Target Audience for Amazon SageMaker Studio for Data Scientists

The Amazon SageMaker Studio for Data Scientists course offers comprehensive training in ML model development, deployment, and management on AWS.

Target Audience:

• Data Scientists looking to leverage SageMaker for machine learning projects
• Machine Learning Engineers focusing on model building and deployment on AWS
• AI/ML Researchers interested in using SageMaker for experimental purposes
• Data Analysts aiming to upscale to predictive analytics and machine learning
• Cloud Solutions Architects designing ML solutions on AWS
• DevOps Engineers responsible for managing ML workflows and infrastructure
• IT Professionals seeking to understand ML operations within SageMaker Studio
• Business Intelligence Professionals expanding their skill set to include ML models
• Technical Product Managers overseeing ML product development
• Software Developers looking to integrate ML into their applications using AWS services

Learning Objectives - What you will Learn in this Amazon SageMaker Studio for Data Scientists?

1. 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.

2. Learning Objectives and Outcomes:

• Launch and navigate the Amazon SageMaker Studio environment from the AWS Service Catalog.
• Process data effectively using SageMaker Studio to ensure it is clean, visualized, analyzed, transformed, and machine learning-ready.
• Establish a repeatable data processing workflow and validate data for machine learning readiness.
• Identify and mitigate bias in datasets and establish baseline model performance.
• Develop, tune, and evaluate machine learning models with SageMaker Studio, considering business goals and industry best practices.
• Implement automatic hyperparameter optimization to enhance model performance.
• Utilize SageMaker Debugger to identify and resolve issues during the model development phase.
• Manage model versions and deployment using SageMaker Model Registry and meet specific use case requirements for inference.
• Automate and orchestrate end-to-end machine learning workflows with Amazon SageMaker Pipelines.
• Set up and schedule model monitoring to detect data and model quality issues, bias drift, and explainability drift, and manage SageMaker Studio resources effectively, including cost management and updates.