Building Data Lakes on AWS Certification Training Course

Candidates can achieve this certification by passing the following exam(s).

Exam Number: Practical Data Science with Amazon SageMaker

The certification exam can be registered and attempted within 3 months of course/module completion at Logitrain training centre on weekdays during normal business hours (excludes public holidays)

• Prepare a dataset for training
• Train and evaluate a Machine Learning model
• Automatically tune a Machine Learning model
• Prepare a Machine Learning model for production
• Think critically about Machine Learning model results

• Developers
• Data Scientists
• Students
• IT professionals
• People interested in Practical Data Science with Amazon SageMaker

• Types of ML
• Job Roles in ML
• Steps in the ML pipeline
• Training and test dataset defined
• Introduction to SageMaker
• Demonstration: SageMaker console
• Demonstration: Launching a Jupyter notebook
• Business challenge: Customer churn
• Review customer churn dataset
• Demonstration: Loading and visualizing your dataset
• Relating features to target variables
• Relationships between attributes
• Demonstration: Cleaning the data
• Types of algorithms
• XGBoost and SageMaker
• Demonstration: Training the data
• Finishing the estimator definition
• Setting hyper parameters
• Deploying the model
• Demonstration: hyper parameter tuning with SageMaker
• Demonstration: Evaluating model performance
• Automatically tune a model
• Automatic hyper parameter tuning with SageMaker
• Tuning jobs
• Deploying a model to an endpoint
• A/B deployment for testing
• Auto Scaling
• Configure and test auto scaling
• Check hyper parameter tuning job
• AWS Auto Scaling
• Set up AWS Auto Scaling
• Cost of various error types
• Binary classification cutoff
• Accessing Amazon SageMaker notebooks in a VPC
• Amazon SageMaker batch transforms
• Amazon SageMaker Ground Truth
• Amazon SageMaker Neo