CLOUD LABS
Getting Started with Amazon Fraud Detector
In this Cloud Lab, you’ll create a custom model using Amazon Fraud Detector, store training data in an S3 bucket, and employ the model in a Python application to detect fraud using Boto3.
beginner
Certificate of Completion
Learning Objectives
Amazon Fraud Detector is an innovative, fully-managed service applying machine learning to identify potentially fraudulent activities. With increasing online transactions and activities, the need for efficient fraud detection has never been higher.
In this Cloud Lab, you’ll create a custom model tailored to your data, utilizing a training dataset stored in an Amazon S3 bucket. You’ll then deploy the model, preparing it for real-world scenarios. Next, you’ll develop a Python application and use the Boto3 library to call the model, detecting fraud in your sample transactions. It’s a sequential journey from understanding the basics to applying them in a live environment.
By completing this Cloud Lab, you’ll be able to detect fraudulent activities and grasp how this technology can be a game-changer in security and compliance. You’ll also understand how machine learning can be practically applied in various domains, enhancing career opportunities in a competitive landscape.
The following is the high-level architecture diagram of the infrastructure that you’ll create in this Cloud Lab:
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I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.
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Your method is simple, straight to the point and I can practice with it everywhere, even from my phone, that's something I have never had in other learning platforms.
![TestimonialsImg](/static/imgs/FelipeMatheus.png)
I highly recommend Educative. The courses are well organized and easy to understand.
![TestimonialsImg](/static/imgs/AdinaOng.png)
I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.
![TestimonialsImg](/static/imgs/CliffordFajardo.png)
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