Learning Amazon SageMaker AI

Amazon SageMaker is a solution for developers who want to deploy predictive machine learning models into a production environment. This course teaches you how to set up and configure SageMaker environments, prepare and preprocess datasets, and build, train, and deploy machine learning models using SageMaker's built-in algorithms. Instructor Kesha Williams guides you through evaluating and optimizing model performance, including hyperparameter tuning and performance monitoring, to ensure your models deliver the best results. Additionally, learn best practices for managing costs, implementing security measures, and operationalizing models with MLOps. Whether new to machine learning or looking to expand your AWS skills, this course will provide the hands-on experience and practical knowledge to use Amazon SageMaker in your projects effectively.

Learning objectives
Set up and configure Amazon SageMaker, including creating and managing SageMaker Studio environments, IAM roles, and permissions.
Prepare and preprocess datasets using SageMaker Data Wrangler and other data preparation tools.
Build, train, and deploy machine learning models using built-in algorithms and the SageMaker SDK.
Evaluate and optimize model performance through hyperparameter tuning and performance monitoring in SageMaker.
implement best practices for cost management, security, and MLOps to operationalize machine learning models effectively in a production environment.