Practical Data Science withAmazon SageMaker
Duration: 1 Day (8 Hours)
Practical Data Science withAmazon SageMaker Course Overview:
This course focuses on applying Machine Learning (ML) to solve real-world use cases using Amazon SageMaker. Participants will gain practical knowledge in the stages of the data science process, including analyzing and visualizing datasets, data preparation, and feature engineering.
The course also covers the practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. A real-life use case of customer retention analysis is explored to provide insights for customer loyalty programs.
By completing this course, participants will acquire the skills needed to effectively utilize Amazon SageMaker for ML projects and generate actionable results for real-world scenarios.
Course Level: Intermediate
Intended audience
This course is intended for:
- Developers
- Data Scientists
Module 1: Introduction to machine learning
- Types of ML
- Job Roles in ML
- Steps in the ML pipeline
Module 2: Introduction to data prep and SageMaker
- Training and test dataset defined
- Introduction to SageMaker
- Demonstration: SageMaker console
- Demonstration: Launching a Jupyter notebook
Module 3: Problem formulation and dataset preparation
- Business challenge: Customer churn
- Review customer churn dataset
Module 4: Data analysis and visualization
- Demonstration: Loading and visualizing your dataset
- Exercise 1: Relating features to target variables
- Exercise 2: Relationships between attributes
- Demonstration: Cleaning the data
Module 5: Training and evaluating a model
- Types of algorithms
- XGBoost and SageMaker
- Demonstration: Training the data
- Exercise 3: Finishing the estimator definition
- Exercise 4: Setting hyper parameters
- Exercise 5: Deploying the model
- Demonstration: hyper parameter tuning with SageMaker
- Demonstration: Evaluating model performance
Module 6: Automatically tune a model
- Automatic hyper parameter tuning with SageMaker
- Exercises 6-9: Tuning jobs
Module 7: Deployment / production readiness
- Deploying a model to an endpoint
- A/B deployment for testing
- Auto Scaling
- Demonstration: Configure and test auto scaling
- Demonstration: Check hyper parameter tuning job
- Demonstration: AWS Auto Scaling
- Exercise 10-11: Set up AWS Auto Scaling
Module 8: Relative cost of errors
- Cost of various error types
- Demo: Binary classification cutoff
Module 9: Amazon SageMaker architecture and features
- Accessing Amazon SageMaker notebooks in a VPC
- Amazon SageMaker batch transforms
- Amazon SageMaker Ground Truth
- Amazon SageMaker Neo
We recommend that attendees of this course have:
- Familiarity with Python programming language
- Basic understanding of Machine Learning
Choose Learning Modality
Discover the perfect fit for your learning journey
Live Online
- Convenience
- Cost-effective
- Self-paced learning
- Scalability
Classroom
- Interaction and collaboration
- Networking opportunities
- Real-time feedback
- Personal attention
Onsite
- Familiar environment
- Confidentiality
- Team building
- Immediate application
