• views: 126
• date: 10 September 2026
• posted by: voska89

Hands-on guide to SageMaker AI with model training, deploying real-time endpoint and running batch transform

What you'll learn

⚡ What Amazon SageMaker AI is
⚡ Set up an IAM role and launch Amazon SageMaker Studio to work with notebooks
⚡ Prepare a dataset and upload it to Amazon S3 for training
⚡ Train a scikit-learn model using SageMaker SDK
⚡ Deploy a trained model to a real-time inference endpoint
⚡ Run offline, large-scale predictions using SageMaker Batch Transform
⚡ High-level view on Model Registry, Pipelines and Monitoring

Requirements

❗ Basic Python
❗ Basic AWS familiarity (S3, IAM roles)
❗ No prior machine learning or data science experience required

Description

This hands-on course provides a practical introduction to machine learning with Amazon SageMaker AI, AWS's fully managed service for building, training, and deploying machine learning models.
If you've ever wanted to take a real dataset, train a machine learning model, deploy it to the cloud, and expose it through a prediction endpoint, this course is for you.
We focus on learning by doing. There are no math-heavy lectures, theory-heavy slides, or assumed data science background. Instead, you'll work through the complete machine learning lifecycle using AWS, Python, and the current SageMaker Python SDK.
We will start by understanding what Amazon SageMaker AI is and how it fits into the modern AWS machine learning ecosystem, including the distinction between Amazon SageMaker AI and the broader Amazon SageMaker platform.
Then we'll move straight into practice.
We will see how to configure IAM permissions, launch SageMaker Studio, prepare a real dataset, and upload it to Amazon S3 before training our machine learning model.
Throughout the course, we will see
✨ Amazon SageMaker AI console
✨ SageMaker Studio
✨ Usage of Amazon S3 for machine learning datasets, inputs, outputs and model packages
✨ SageMaker Python SDK
✨ Model training
✨ Model deployment
✨ Real-time inference endpoints
✨ Invoking deployed models from Python
✨ Batch Transform for offline predictions
✨ SageMaker Model Registry
✨ SageMaker Pipelines
✨ Amazon CloudWatch
✨ IAM roles and permissions
✨ Cost optimization and resource cleanup
Everything is explained through practical examples, with the code provided so you can focus on understanding how the different parts of the machine learning workflow fit together.
This course is a great fit if you are
✨ A developer interested in machine learning on AWS
✨ A cloud engineer exploring AI and ML services
✨ A DevOps engineer moving toward MLOps
✨ A software engineer wanting to deploy machine learning models
✨ Anyone looking for a practical introduction to Amazon SageMaker AI
Basic Python and AWS familiarity will be helpful, but no previous SageMaker or machine learning experience is required.
If you want a clear, beginner-friendly, and hands-on introduction to machine learning with Amazon SageMaker AI, this course is for you.

Who this course is for

⭐ A developer interested in machine learning on AWS
⭐ A cloud engineer exploring AI and ML services
⭐ A DevOps engineer moving toward MLOps
⭐ A software engineer wanting to deploy machine learning models
⭐ Anyone looking for a practical introduction to Amazon SageMaker AI

Homepage

https://www.udemy.com/course/hands-on-introduction-to-machine-learning-with-aws-sagemaker

   
   
   

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