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Course Outline
Introduction
- Understanding Machine Learning with SageMaker for Government
- Machine Learning Algorithms for Government Applications
Overview of AWS SageMaker Features
- AWS and Cloud Computing for Government
- Model Development for Government Projects
Setting up AWS SageMaker for Government
- Creating an AWS Account for Government Use
- Configuring IAM Admin User and Group for Government Security
Familiarizing with SageMaker Studio for Government Users
- User Interface Overview for Government Personnel
- Using Studio Notebooks for Government Data Analysis
Preparing Data Using Jupyter Notebooks for Government Projects
- Notebooks and Libraries for Government Data Processing
- Creating a Notebook Instance for Government Use
Training a Model with SageMaker for Government Applications
- Training Jobs and Algorithms for Government Models
- Data and Model Parallel Training for Government Scalability
- Post-Training Bias Analysis for Government Fairness and Accountability
Deploying a Model in SageMaker for Government Operations
- Model Registry and Model Monitor for Government Compliance
- Compiling and Deploying Models with Neo for Government Efficiency
- Evaluating Model Performance for Government Decision-Making
Cleaning Up Resources for Government Projects
- Deleting Endpoints for Government Resource Management
- Deleting Notebook Instances for Government Cost Control
Troubleshooting for Government Users
Summary and Conclusion for Government Applications
Requirements
- Experience in application development for government projects
- Familiarity with the Amazon Web Services (AWS) Console
Audience
- Data scientists
- Developers
21 Hours
Testimonials (2)
I've find out new interesting things about Lambda and Serverless
Oleg Buldumac - PUBLIC COURSE
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