Course Outline
Introduction
- Understanding machine learning with SageMaker
- Machine learning algorithms
Overview of AWS SageMaker Features
- AWS and cloud computing
- Models development
Setting up AWS SageMaker
- Creating an AWS account
- IAM admin user and group
Familiarizing with SageMaker Studio
- UI overview
- Studio notebooks
Preparing Data Using Jupyter Notebooks
- Notebooks and libraries
- Creating a notebook instance
Training a Model with SageMaker
- Training jobs and algorithms
- Data and model parallel trainings
- Post-training bias analysis
Deploying a Model in SageMaker
- Model registry and model monitor
- Compiling and deploying models with Neo
- Evaluating model performance
Cleaning Up Resources
- Deleting endpoints
- Deleting notebook instances
Troubleshooting
Summary and Conclusion
Requirements
- Experience with application development
- Familiarity with Amazon Web Services (AWS) Console
Audience
- Data scientists
- Developers
Testimonials (5)
The trainer knew exactly what they were speaking about.
Madumetsa Msomi - BMW
Course - AWS DevOps Engineers
All good, nothing to improve
Ievgen Vinchyk - GE Medical Systems Polska Sp. Z O.O.
Course - AWS Lambda for Developers
IOT applications
Palaniswamy Suresh Kumar - Makers' Academy
Course - Industrial Training IoT (Internet of Things) with Raspberry PI and AWS IoT Core 「4 Hours Remote」
I liked getting to understand the breadth of the services offered by AWS and gaining a better understanding of their pricing model for each of those services.
William Crowdis - MDA Systems Ltd.
Course - AWS Developer Associate
Gabriel was very organized and prepared for this training. He answered all questions and clarify the AWS notions and architecture. Great job, Gabriel.