Description

Introduction

AWS SageMaker for Data Scientists: A Beginner’s Guide is tailored for aspiring and early-career data scientists who want to leverage AWS SageMaker to build, train, and deploy machine learning models efficiently. As a fully managed service, SageMaker simplifies the end-to-end machine learning workflow, allowing data scientists to focus on data exploration and model development without worrying about infrastructure. This course helps you get hands-on with SageMaker while learning best practices in model lifecycle management.

Prerequisites

To follow this course effectively, learners should have:

• Basic knowledge of Python programming.

• Foundational understanding of machine learning concepts (e.g., supervised vs. unsupervised learning).

• Familiarity with Jupyter notebooks.

• An AWS account with SageMaker permissions enabled.

• Some experience working with datasets (e.g., using pandas or NumPy).

Table of Contents

1. Introduction to AWS SageMaker

   • 1.1 What is SageMaker?

   • 1.2 Core Components (Studio, Notebooks, Training, Inference)

   • 1.3 Advantages for Data Scientists

2. SageMaker Studio and Notebook Environments

   • 2.1 Setting Up SageMaker Studio

   • 2.2 Using Jupyter Notebooks in SageMaker

   • 2.3 Working with Git Repositories and Data Sources

3. Data Preparation in SageMaker

   • 3.1 Loading Data from S3 or Local Sources

   • 3.2 Exploratory Data Analysis (EDA)

   • 3.3 Feature Engineering with Pandas and SageMaker Processing

4. Model Development and Training

   • 4.1 Choosing Between Built-in, Pretrained, and Custom Models

   • 4.2 Using SageMaker Estimators and Script Mode

   • 4.3 Running Training Jobs with Managed Infrastructure

5. Model Evaluation and Optimization

   • 5.1 Evaluating Model Accuracy and Metrics

   • 5.2 Hyperparameter Tuning with SageMaker

   • 5.3 Model Debugging and Profiling

6. Model Deployment and Inference

   • 6.1 Deploying to Real-time Endpoints

   • 6.2 Batch Transform for Offline Predictions

   • 6.3 Monitoring Models with SageMaker Model Monitor

7. Automation and Pipelines

   • 7.1 Introduction to SageMaker Pipelines

   • 7.2 Creating Reproducible ML Workflows

   • 7.3 Integrating with CI/CD Tools (e.g., CodePipeline)

8. Cost Management and Best Practices

   • 8.1 Choosing the Right Instance Types

   • 8.2 Auto-shutdown and Resource Cleanup

   • 8.3 IAM Roles, Security, and Governance

AWS SageMaker empowers data scientists to go from idea to production with ease, providing robust tools for every step of the machine learning lifecycle. By completing this beginner’s guide, you now have a solid foundation in building, training, evaluating, and deploying ML models within a scalable cloud-based environment. You’re now equipped to explore advanced topics like deep learning, model explainability, and MLOps on AWS.