This is a self-paced lab that takes place in the Google Cloud console. FraudFinder is a series of JupyterLabs that show how to implement an end-to-end Data to AI architecture works on Google Cloud, through a toy use case of real-time fraud detection system.
Data Analysis with the FraudFinder Workshop
Data Analysis with the FraudFinder Workshop
Instructor: Google Cloud Training
Access provided by Genesco Inc
Build in-demand job skills with step-by-step instructions
Beginner level
No prior experience required
1 hour 30 minutes
Learn at your own pace
Hands-on learning
What you'll learn
To read historical payment transactions data stored in a data warehouse and from a live stream of new transactions, perform exploratory data analysis
Feature engineering & ingest features into a Feature Store. Train a model using Feature Store. Register your model in a model registry
Deploy your model to an endpoint. Real-time inference on your model with Feature Store. Monitor your model.
Skills you'll practice
- Continuous Monitoring
- Real Time Data
- Jupyter
- Machine Learning
- Applied Machine Learning
- Data Warehousing
- Statistical Modeling
- Anomaly Detection
- Data Analysis
- Feature Engineering
- MLOps (Machine Learning Operations)
- Predictive Modeling
- Application Deployment
- Exploratory Data Analysis
- Data Store
- Data Processing
- Google Cloud Platform
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
No downloads or installation required
Only available on desktop
See how employees at top companies are mastering in-demand skills
Learn, practice, and apply job-ready skills in less than 2 hours
- Receive training from industry experts
- Gain hands-on experience solving real-world job tasks
About this project
Instructor
Offered by
How you'll learn
Hands-on, project-based learning
Practice new skills by completing job-related tasks with step-by-step instructions.
No downloads or installation required
Access the tools and resources you need in a cloud environment.
Available only on desktop
This project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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