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Practical Data Science with Amazon SageMaker

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1 Day Live Online
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ASPE is an IIBA Endorsed Education Provider of business analysis training. Select Project Delivery courses offer IIBA continuing development units (CDU) in accordance with IIBA standards.


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In this intermediate-level course, individuals learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use cases include customer retention analysis to inform customer loyalty programs.

Prepare a dataset for training
Train and evaluate a Machine Learning mode
Automatically tune a Machine Learning model
Prepare a Machine Learning model for production
Think critically about Machine Learning model results
Upcoming Dates and Locations
All Live Online times are listed in Eastern Time Guaranteed To Run

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Course Outline

Module 1: Introduction to Machine Learning

  1. Types of ML
  2. Job Roles in ML
  3. Steps in the ML pipeline

Module 2: Introduction to Data Prep and SageMaker

  1. Training and Test dataset defined
  2. Introduction to SageMaker

Demo: SageMaker console
Demo: Launching a Jupyter notebook

Module 3: Problem formulation and Dataset Preparation

  1. Business Challenge: Customer churn
  2. Review Customer churn dataset

Module 4: Data Analysis and Visualization

Demo: Loading and Visualizing your dataset
Exercise 1: Relating features to target variables
Exercise 2: Relationships between attributes

Demo: Cleaning the data

Module 5: Training and Evaluating a Model

  1. Types of Algorithms
  2. XGBoost and SageMaker

Demo 5: Training the data
Exercise 3: Finishing the Estimator definition
Exercise 4: Setting hyperparameters
Exercise 5: Deploying the model
Demo: Hyperparameter tuning with SageMaker
Demo: Evaluating Model Performance

Module 6: Automatically Tune a Model

  1. Automatic hyperparameter tuning with SageMaker

Exercises 6-9: Tuning Jobs

Module 7: Deployment / Production Readiness

  1. Deploying a model to an endpoint
  2. A/B deployment for testing
  3. Auto Scaling Scaling

Demo: Configure and Test Autoscaling
Demo: Check Hyperparameter tuning job
Demo: AWS Autoscaling
Exercise 10-11: Set up AWS Autoscaling

Module 8: Relative Cost of Errors

Who should attend
  • Developers
  • Data Scientists