🚀 Real-World Use Cases

GCP ML Engineer
Use Cases

Five production-ready ML solutions built on Google Cloud Platform — from demand forecasting to real-time fraud detection. Each use case includes a deep-dive study guide and a hands-on Python notebook you can run in Google Colab.

Start with Demand Forecasting → ← Back to GCP MLE Hub
5
Use Cases
5
Notebooks
GCP
Native
MLOps
Pipelines
🌐
Open Source

Use Cases

Real problems. Real solutions. GCP-native architecture.

01
E-commerce Demand Forecasting
Retailers lose $1.1T/year from inventory distortion. Build a time-series forecasting pipeline with BigQuery ML ARIMA_PLUS, handle seasonality and promotions, and deploy batch predictions via Vertex AI.
BigQuery ML ARIMA_PLUS Vertex AI
02
Real-Time Fraud Detection
Credit card fraud costs $32B/year. Build a real-time fraud detection system with Feature Store for online serving, streaming predictions via Dataflow, and model monitoring for concept drift.
Feature Store Pub/Sub XGBoost
03
Customer Churn Prediction Pipeline
SaaS companies lose 5-7% of customers monthly. Build an end-to-end Vertex AI Pipeline with BigQuery feature engineering, XGBoost training, Vizier hyperparameter tuning, and automated retraining.
Vertex AI Pipelines Vizier KFP
04
Manufacturing Defect Detection
Manual visual inspection catches only 80% of defects. Use AutoML Vision and custom CNNs for defect classification, deploy to edge with Edge TPU, and track precision/recall per defect type.
AutoML Vision Edge TPU Transfer Learning
05
Product Recommendation Engine
E-commerce conversion rates average only 2-3%. Build a hybrid recommendation system with BigQuery ML matrix factorization, content-based filtering, and real-time serving via Feature Store.
Matrix Factorization Feature Store A/B Testing