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Amazon SageMaker Model Deployment with Lambda & API Gateway

Name: Amazon SageMaker Model Deployment with Lambda &amp; API Gateway
Author: Baturi

Amazon SageMaker • Amazon Lambda Amazon • API Gateway
What you'll learn
✓ Create and train a SageMaker ML Model with XGBoost
✓ Deploy the SageMaker Model to a model endpoint
✓ Create a Lambda Function to invoke the model endpoint
✓ Create an API Gateway app to invoke the Lambda Function
✓ Create a VPC endpoint
✓ Launch and EC2 instance and connect to it via SSH
✓ Run a Linux cURL command to make a PUT request to the API
Requirements
● Some Python programming is helpful but not necessary. Some basic API knowledge is helpful but not necessary
Description
Disclaimer: This course contains the use of artificial intelligence.
Due to a disability, I have used AI to assist with the production and editing of the audio in this course. The preview/promo video uses an AI generator of my image – but it is my image. I also use a "text-to-speech" AI tool to create an AI model of my voice recorded from an earlier date.
Oh, and I used it to create the image used in Udemy's search results and in a couple of slides.
The ideas, most of the slides, script, demos, and the downloadable materials are of my own production. I hope this does not dissuade you from this course.
Now, back to the course description.
~ Joe Cline
Do you work in an AWS cloud environment and want to level up your skills by deploying a real machine learning model into production? Have you trained models before but felt unsure how to turn them into something users or applications can actually call?
If that sounds familiar, you're in the right place.
Welcome to Amazon SageMaker Model Deployment with Lambda and API Gateway—a practical, hands-on course designed to help you connect the dots between machine learning and real-world AWS services.
In this course, you won't just train a model and stop there. You'll learn the complete deployment workflow, starting with building and training a machine learning model in Amazon SageMaker, deploying it to a managed SageMaker endpoint, and then exposing that model through a fully functional HTTP prediction API using AWS Lambda and Amazon API Gateway. By the end, you'll understand how modern ML-powered applications are actually built in the cloud.
To keep things realistic, we'll work with real data from the UCI Bank Marketing Database, and you'll have access to the full data dictionary through the course downloads. Using this dataset, we'll explore and answer a meaningful business question
"Which bank customers are most likely to purchase a term deposit account?"
Every step of the process is demonstrated clearly, with all code provided in a downloadable JupyterLab notebook. You can follow along line by line, experiment freely, and reuse the project as a starter template for your own machine learning deployments.
Whether you're a cloud engineer, data scientist, or ML practitioner looking to make your models production-ready, this course gives you practical, reusable skills you can apply immediately. No fluff—just clear explanations, real AWS services, and hands-on experience that will boost your confidence and your career.
Let's turn your machine learning models into live, scalable APIs that actually deliver value!
Who this course is for
■ AWS platform engineers, Data Engineers, Data Scientist, ML Engineers and Application developers
Homepage

https://www.udemy.com/course/amazon-sagemaker-model-deployment-with-lambda-api-gateway/

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Amazon SageMaker Model Deployment with Lambda & API Gateway

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