AWS SageMaker: training, deploying and monitoring models on AWS
How to run a real machine learning project on AWS SageMaker, from data on S3 to a monitored endpoint, with the invoice in view at every step.
Course Duration: 7h
What You'll Learn
- Position SageMaker's components against the ML lifecycle you already know
- Configure Studio, notebooks and IAM with just enough privilege
- Store data on S3 in a shape SageMaker training jobs can consume
- Launch training jobs with a built-in container, then with your own script
- Tune hyperparameters and deploy real-time, serverless or batch inference
- Automate the whole flow with SageMaker Pipelines and the Model Registry
- Watch data drift with Model Monitor and cap the invoice with budgets
Prerequisites
- Course 20 (MLOps): experiment tracking, model registry, monitoring
- Working knowledge of AWS S3 and IAM
- Python, pandas, scikit-learn and XGBoost basics
Course Modules
- Overview of SageMaker and its components
- Studio, notebooks and working environments
- Data on S3 and the Feature Store
- Training jobs and built-in containers
- Custom containers and training scripts
- Automatic hyperparameter tuning
- Real-time and serverless endpoints
- Batch transform for large volumes
- SageMaker Pipelines and the Model Registry
- Monitoring, alerts and cost control
The running example
The churn model from course 20 is ported to SageMaker, one module at a time. Data lands on S3 in module 3, XGBoost trains on it in module 4, a custom scikit-learn script replaces it in module 5, tuning refines it in module 6, endpoints serve it in modules 7 and 8, a pipeline orchestrates everything in module 9, and Model Monitor plus a budget close the loop in module 10. Every module prints the estimated cost of what you launched.
Assessment and certificate
The course ends with a 40-question exam covering every module. On success, a certificate of completion is issued; its number is verifiable on the platform.
Free courses, by contrast, end with a 5-question quiz and a preview of the certificate, without certification.