Description

Introduction

Data Preparation and Feature Engineering with SageMaker is a practical course for data scientists, ML engineers, and analysts looking to optimize the early stages of the machine learning pipeline. Proper data preparation and robust feature engineering are essential to building high-performance models. In this course, you’ll learn how to use AWS SageMaker’s built-in tools and frameworks to clean, transform, and enrich datasets efficiently at scale.

Prerequisites

To get the most out of this course, participants should have:

• Basic knowledge of Python and pandas.

• Foundational understanding of machine learning workflows.

• Familiarity with Jupyter notebooks.

• An active AWS account with SageMaker and S3 access.

Table of Contents

1. Introduction to Data Preparation in SageMaker

   • 1.1 Importance of Data Preparation and Feature Engineering

   • 1.2 SageMaker Tools for Data Transformation

   • 1.3 End-to-End ML Pipeline Overview

2. Setting Up Your Environment

   • 2.1 Launching SageMaker Studio or Notebooks

   • 2.2 Connecting to Amazon S3 for Data Storage

   • 2.3 Creating SageMaker Processing Jobs

3. Data Ingestion and Cleaning

   • 3.1 Reading Data from CSV, Parquet, and Databases

   • 3.2 Handling Missing Values and Duplicates

   • 3.3 Data Type Conversions and Normalization

4. Exploratory Data Analysis (EDA)

   • 4.1 Statistical Summaries and Visualizations

   • 4.2 Correlation Analysis

   • 4.3 Identifying Data Quality Issues

5. Feature Engineering Techniques

   • 5.1 Encoding Categorical Variables (One-Hot, Label Encoding)

   • 5.2 Feature Scaling (Standardization, Normalization)

   • 5.3 Date/Time and Text Feature Extraction

6. Using SageMaker Data Wrangler

   • 6.1 Overview of Data Wrangler Interface

   • 6.2 Building Transformation Workflows Visually

   • 6.3 Exporting and Integrating with SageMaker Pipelines

7. Advanced Feature Engineering with SageMaker Processing

   • 7.1 Using Custom Python Scripts for Transformation

   • 7.2 Scalable Data Processing with SageMaker Pipelines

   • 7.3 Reusable Feature Engineering Pipelines

8. Feature Store and Feature Reuse

   • 8.1 Introduction to SageMaker Feature Store

   • 8.2 Creating Feature Groups and Ingesting Data

   • 8.3 Querying, Updating, and Reusing Features

Effective data preparation and feature engineering form the foundation of high-performing ML models. AWS SageMaker offers a powerful suite of tools—like Data Wrangler, Processing Jobs, and Feature Store—that enable data scientists to build scalable and reusable pipelines. By mastering these techniques, you’ll significantly improve the accuracy, reliability, and maintainability of your machine learning solutions.