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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
21 Hours
Testimonials (5)
Trainer had good grasp of concepts
Josheel - Verizon Connect
Course - Amazon Redshift
Amount of Information, Exercises
Lukasz Kowalski - Sii Sp. z o.o.
Course - AWS IoT Core
Machine Translated
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」
Later the balance between theory and practice was much better. But the beginnings were terrible. The way of expressing (language) is very calm, understandable, in a human way.
Lukasz Derkowski - NetworkedAssets Sp. z o.o.
Course - AWS CloudFormation
Machine Translated
