Amazon Web Services (AWS)
Practical Data Science with Amazon SageMaker
A practical day of data science with SageMaker.
1 dayIntermediate9 modulesAWS Machine Learning Engineer - Associate
About this course
A practical day of data science with SageMaker. You will frame a business problem, prepare and analyse data, train and evaluate an XGBoost model, tune hyperparameters automatically, weigh the cost of errors and deploy to a scalable endpoint.
What you'll learn
- Prepare datasets for machine learning with SageMaker
- Train, evaluate, and tune ML models using XGBoost
- Perform hyperparameter tuning with SageMaker tools
- Deploy models to production endpoints with autoscaling
- Analyze model outputs and consider the cost of errors
Curriculum
9 modules
- Types of machine learning
- ML job roles and pipeline stages
- Training vs. test datasets
- SageMaker console walkthrough
- Launching Jupyter notebooks
- Business challenge: customer churn
- Exploring and preparing the dataset
- Visualizing features and target relationships
- Cleaning and transforming data
- Using XGBoost in SageMaker
- Setting up estimators and hyperparameters
- Deploying and evaluating the model
- Creating tuning jobs in SageMaker
- Exercises in parameter optimization
- Endpoint deployment
- A/B testing and autoscaling
- Monitoring performance
- Error types and business implications
- Adjusting classification thresholds
- SageMaker in a VPC
- Batch transforms, Ground Truth, Neo
Who it's for
Developers and analysts with basic machine learning knowledge who want hands-on practice with SageMaker.
Before you start
- Familiarity with Python programming language
- Basic understanding of Machine Learning
Prepares for
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