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Course Overview
Turning data into actionable insights requires more than theory; you need tools that scale. In this one-day, hands-on course, you’ll learn how to build, train, and deploy machine learning models using Amazon SageMaker. You’ll follow a complete end-to-end data science workflow, from data prep and visualization to model evaluation and tuning, all within the SageMaker platform.
Through a customer churn use case, you'll apply real-world techniques like feature engineering, hyperparameter tuning, and autoscaling. Whether you're a developer or data scientist, this course will strengthen your ability to think critically about model performance and production-readiness using SageMaker’s powerful features.
Who Should Attend
Developers and Data Scientists.
Course Objectives
By the end of the course, you’ll be able to execute a full machine learning pipeline using Amazon SageMaker. You’ll develop practical skills in model training, tuning, and deployment that apply to real-world business problems.
- 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
Course Outline
Module 1: Introduction to Machine Learning
- Types of machine learning
- ML job roles and pipeline stages
Module 2: Data Preparation and SageMaker Overview
- Training vs. test datasets
- SageMaker console walkthrough
- Launching Jupyter notebooks
Module 3: Problem Formulation and Dataset Prep
- Business challenge: customer churn
- Exploring and preparing the dataset
Module 4: Data Analysis and Visualization
- Visualizing features and target relationships
- Cleaning and transforming data
Module 5: Training and Evaluating the Model
- Using XGBoost in SageMaker
- Setting up estimators and hyperparameters
- Deploying and evaluating the model
Module 6: Automatic Hyperparameter Tuning
- Creating tuning jobs in SageMaker
- Exercises in parameter optimization
Module 7: Deployment and Production Readiness
- Endpoint deployment
- A/B testing and autoscaling
- Monitoring performance
Module 8: Understanding Cost of Errors
- Error types and business implications
- Adjusting classification thresholds
Module 9: Amazon SageMaker Architecture & Features
- SageMaker in a VPC
- Batch transforms, Ground Truth, Neo