Skip to main content

ATP Detect Anomalies in Game Transactions with ML and Sagemaker


Onedata_University
Enrollment in this course is by invitation only

Course Overview:

About This Course

Game studios that are building and operating multiple games tend to redo much of the server-side validation of transactional data received from game clients. This course covers the use of a central model (or multiple models per game) for offloading server processing and improving server response time. The course reviews the different anomalies associated with game transaction data and how machine learning (ML) can help perform validations. Course objectives: This course is designed to teach you how to: •Understand game transactions and associated data •Recognize anomalies in game transactions •Review example game report data •Understand machine learning architecture for performing validations Intended audience: This course is intended for: •Game developers •Data analysts who work with game transactions Prerequisites: We recommend that attendees of this course have: •Understanding of basic gaming concepts •Basic understanding of machine learning Course outline:: •Game transactions •Anomalies •Game report data •How can ML help •Demo

Requirements

Basic Knowledge of AWS Services and SQL

Course Staff

Srinivasan Lingaraj

Solutions Architect

Mohammed Arief M

AWS Technical Trainer

Frequently Asked Questions

What web browser should I use?

The Upskilled platform works best with current versions of Chrome, Edge, Firefox, or Safari.

See our list of supported browsers for the most up-to-date information.