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:
- Basic understanding of cloud computing concepts, particularly within the AWS ecosystem.
- Familiarity with AWS core services such as Amazon S3, Amazon EC2, and IAM (Identity and Access Management).
- Knowledge of fundamental data science concepts, including data cleaning, transformation, and visualization techniques.
- Experience with Python programming, as it is commonly used for scripting in data science tasks and machine learning workflows.
- Awareness of machine learning principles and some experience with ML models is beneficial, although deep expertise is not a requirement.
- Ability to navigate and operate within a Linux-based environment, as this is often the underlying OS for cloud-based data science tools.
- Comfort with using command-line interfaces (CLI) and development environments such as Jupyter Notebooks.
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.
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?
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:
- 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.
