Amazon SageMaker Studio for Data Scientists Training
Duration
Prerequisites
- Complete AWS Technical Essentials course or have equivalent experience.
- Individuals who are not experienced data scientists should complete The Machine Learning Pipeline on AWS and Deep Learning on AWS courses then
- Have 1-year on-the-job experience building models
Target Audience
- Experienced data scientists who are proficient in ML and deep learning fundamentals.
- Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.
Skills Gained
- Amazon SageMaker Setup and Navigation
- Launch SageMaker Studio from the AWS Service Catalog.
- Navigate the SageMaker Studio UI.
- Data Processing
- Use Amazon SageMaker Studio to collect, clean, visualize, analyze, and transform data.
- Set up a repeatable process for data processing.
- Use SageMaker to validate that collected data is ML ready.
- Detect bias in collected data and estimate baseline model accuracy.
- Model Development
- Use Amazon SageMaker Studio to develop, tune, and evaluate an ML model against business objectives and fairness and explainability best practices.
- Fine-tune ML models using automatic hyperparameter optimization capability.
- Use SageMaker Debugger to surface issues during model development.
- Demo 2: Autopilot
- Deployment and Inference
- Use Model Registry to create a model group; register, view, and manage model versions; modify model approval status; and deploy a model.
- Design and implement a deployment solution that meets inference use case requirements.
- Create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines.
- Monitoring
- Configure a SageMaker Model Monitor solution to detect issues and initiate alerts for changes in data quality, model quality, bias drift, and feature attribution (explainability) drift.
- Create a monitoring schedule with a predefined interval.
- Managing SageMaker Studio Resources and Updates
- List resources that accrue charges.
- Recall when to shut down instances.
- Explain how to shut down instances, notebooks, terminals, and kernels.
- Understand the process to update SageMaker Studio.
- Capstone
- The Capstone lab will bring together the various capabilities of SageMaker Studio discussed in this course. Students will be given the opportunity to prepare, build, train, and deploy a model using a tabular dataset not seen in earlier labs. Students can choose among basic, intermediate, and advanced versions of the instructions.
- Capstone Lab: Build an End-to-End Tabular Data ML Project Using SageMaker Studio and the SageMaker Python SDK
Partner Registration
The course you are registering for is being delivered by our sister company - ExitCertified. All logistics related to course delivery will be managed by the ExitCertified team. If you have a dedicated Web Age representative, please feel to reach out to them with any questions/concerns you may have.
You'll now be redirected to https://www.exitcertified.com to complete the enrollment process.
Partner Registration
The course you are registering for is being delivered by our sister company - ExitCertified. All logistics related to course delivery will be managed by the ExitCertified team. If you have a dedicated Web Age representative, please feel to reach out to them with any questions/concerns you may have.
You'll now be redirected to https://www.exitcertified.com to complete the enrollment process.
