Learn Amazon SageMaker
A guide to building, training, and deploying machine learning models for developers and data scientists
Created by Julien Simon
Explore how to build, train, and deploy machine learning models using Amazon SageMaker. Through real-world Python examples, you'll work with tabular data, computer vision, and natural language processing. Gain hands-on experience automating workflows and optimizing models for production.
Packt | Aug 2020 | 490 min
What You Will Learn
You'll start by setting up SageMaker and working through practical examples using Python and Jupyter notebooks. Each section focuses on a different aspect of the ML workflow, from data preparation to deployment. Along the way, you'll apply best practices for automation, monitoring, and model optimization.
Key Features
- Build and deploy machine learning models efficiently with SageMaker APIs and tools
- Use AutoML and built-in algorithms to handle diverse data types and real-world tasks
- Monitor, debug, and optimize models to improve accuracy and reduce deployment costs
Target Audience
Designed for software engineers, data scientists, and ML developers with basic AWS and Python knowledge. If you want to streamline machine learning workflows and deploy models without managing infrastructure, this course will help you build confidence and skill with SageMaker.
