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Training
Practical Data Science with Amazon SageMaker

Practical Data Science with Amazon SageMaker

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.

Training code
AWS-PDS
Duration
1 day
Level
Average
Price
2300 zł
Who is it for?

This course is intended for:

  • Developers
  • Data Scientists
Acquired skills

In this course, you will learn how to:

  • 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
Prerequisites
  • Familiarity with Python programming language
  • Basic understanding of Machine Learning
Scope of training

Introduction to Machine Learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline

Introduction to Data Prep and SageMaker

  • Training and Test dataset defined
  • Introduction to SageMaker
  • Demo: SageMaker console
  • Demo: Launching a Jupyter notebook

Problem formulation and Dataset Preparation

  • Business Challenge: Customer churn
  • Review Customer churn dataset

Data Analysis and Visualization

  • Demo: Loading and Visualizing your dataset
  • Exercise: Relating features to target variables
  • Exercise: Relationships between attributes
  • Demo: Cleaning the data

Training and Evaluating a Model

  • Types of Algorithms
  • XGBoost and SageMaker
  • Demo: Training the data
  • Exercise: Finishing the Estimator definition
  • Exercise: Setting hyperparameters
  • Exercise: Deploying the model
  • Demo: Hyperparameter tuning with SageMaker
  • Demo: Evaluating Model Performance

Automatically Tune a Model

  • Automatic hyperparameter tuning with SageMaker
  • Exercises: Tuning Jobs

Deployment / Production Readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling Scaling
  • Demo: Configure and Test Autoscaling
  • Demo: Check Hyperparameter tuning job
  • Demo: AWS Autoscaling
  • Exercise: Set up AWS Autoscaling

Relative Cost of Errors

  • Cost of various error types
  • Demo: Binary Classification cutoff

Amazon SageMaker Architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

Enquire about the date and quotation

This training is available on request. Let us know and we will arrange a date and format (online/in-person).

We will respond with a proposed date within 24 hours
No obligation – simply enquiring does not reserve anything
Available exclusively for your team