This is the repository for the LinkedIn Learning course course-name. The full course is available from LinkedIn Learning.
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.
See the readme file in the main branch for updated instructions and information.
- To use these exercise files, you must have the following installed:
- Jupyter Notebook environment in the cloud or locally, with the necessary libraries installed
- Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
Kesha Williams
Award-Winning Tech Innovator and AI/ML Leader
Check out my other courses on LinkedIn Learning.