AWSPDSASM - Practical Data Science with Amazon SageMaker

You will 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 case includes customer retention analysis to inform customer loyalty programs.

INR 15000 + tax

Date : 22 Jan 2024

Code: awspdsasm

Duration: 1.0 day

other dates


Virtual ILT | 22 Jan 2024 - 22 Jan 2024
Virtual ILT | 26 Feb 2024 - 26 Feb 2024
Virtual ILT | 18 Mar 2024 - 18 Mar 2024
Virtual ILT | 15 Apr 2024 - 15 Apr 2024
Virtual ILT | 27 May 2024 - 27 May 2024
Virtual ILT | 03 Jun 2024 - 03 Jun 2024

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  • 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


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 Sage Maker

  • Training and test dataset defined
  • Introduction to Sage Maker
  • Demonstration: Sage Maker console
  • Demonstration: Launching a Jupiter notebook

Module 3: Problem formulation and dataset preparation

  • Business challenge: Customer churn
  • Review the 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 Sage Maker
  • Demonstration: Training the data
  • Exercise 3: Finishing the estimator definition
  • Exercise 4: Setting hyperparameters
  • Exercise 5: Deploying the model
  • Demonstration: hyperparameter tuning with Sage Maker
  • Demonstration: Evaluating model performance

Module 6: Automatically tune a model

  • Automatic hyperparameter tuning with Sage Maker
  • 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 Sage Maker architecture and features

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


This course is intended for:

  • Developers
  • Data Scientists


We recommend that attendees of this course have:

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning



Course Benefits

  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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