Getting Started with Amazon SageMaker Studio
Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
Created by Michael Hsieh
Explore how to manage the entire machine learning workflow using Amazon SageMaker Studio. You will learn to prepare data, build and train models, and deploy solutions in a single, integrated environment. Gain practical experience that helps you deliver real-world ML projects efficiently.
Packt | Mar 2022 | 326 min
What You Will Learn
You will follow hands-on examples that walk through real-life machine learning projects in SageMaker Studio. Each step is explained clearly, from setting up data to deploying models, so you can apply what you learn right away. Practical tips and recommendations help you build confidence as you progress.
Key Features
- Master the end-to-end ML workflow in SageMaker Studio, from data prep to deployment
- Learn to scale, monitor, and operationalize ML models for production environments
- Boost productivity by using SageMaker Studio's integrated tools and best practices
Target Audience
Ideal for data scientists and machine learning engineers with basic ML knowledge who want to become proficient in Amazon SageMaker Studio. If you are looking to manage the full ML lifecycle in the cloud and streamline your workflow, you will benefit from the practical, step-by-step guidance provided here.
