TrainingArtificial intelligence / AWS Data ScientistAWS-ASSDS
AWS-ASSDSAWS · authorised training
Amazon SageMaker Studio for Data Scientists
The course prepares experienced data scientists to use the tools of Amazon SageMaker Studio at every stage of the ML model lifecycle – from data preparation, through building, training and tuning, to deployment and monitoring. It covers SageMaker Data Wrangler, Feature Store, Experiments, Debugger, Clarify, Pipelines and Model Monitor, as well as the Amazon CodeWhisperer and Amazon CodeGuru Security extensions. Sessions combine presentations, hands-on labs, demonstrations, discussions and a capstone project.
- Duration
- 3 days
- Level
- Advanced
- Provider
- AWS
- Topic
- Artificial intelligence / AWS Data Scientist
Course outline
- Amazon SageMaker Studio setup and JupyterLab extensions
- Data processing – Data Wrangler, Amazon EMR, AWS Glue interactive sessions, SageMaker Processing, Feature Store
- Model development – training jobs, own scripts and containers, Experiments, Debugger, Autopilot, Clarify, JumpStart
- Deployment and inference – Model Registry, Pipelines, inference options, scaling, testing and optimisation
- Model monitoring with Amazon SageMaker Model Monitor
- Managing SageMaker Studio resources and updates – accrued cost and shutting down resources
- Capstone project – full ML lifecycle from data preparation to batch predictions
Skills you will gain
- Prepare, build, train, deploy and monitor ML models in Amazon SageMaker Studio
- Analyse and prepare data with SageMaker Data Wrangler and Amazon EMR
- Process data in SageMaker Processing using the SageMaker Python SDK
- Create features and feature groups in SageMaker Feature Store
- Track training and tuning iterations with SageMaker Experiments
- Analyse training runs and set alerts with SageMaker Debugger
- Evaluate model bias and explainability with SageMaker Clarify
- Automate model development with SageMaker Pipelines and SageMaker Model Registry
- Run inference and batch predictions using model endpoints
- Monitor models with Amazon SageMaker Model Monitor
Who should attend
- Experienced data scientists proficient in machine learning and deep learning fundamentals
- Data scientists responsible for training, tuning and deploying ML models
Prerequisites
- Experience using ML frameworks
- Python programming experience
- At least one year of experience as a data scientist responsible for training, tuning and deploying models
- AWS Technical Essentials training (digital or classroom)
6500 PLN
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