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.
E-commerce Demand Forecasting
Predict product-level demand across 10,000+ SKUs using BigQuery ML ARIMA_PLUS and Vertex AI custom models. Reduce overstock by 23%, eliminate stockouts by 31%, and save millions annually with a fully automated, GCP-native forecasting pipeline.
Real-Time Fraud Detection
Build a production-grade, real-time fraud detection system on Google Cloud Platform. From streaming ingestion with Pub/Sub through feature engineering in Dataflow to sub-50ms online predictions with Vertex AI — every component maps directly to the GCP Professional Machine Learning Engine
Customer Churn Prediction Pipeline
Build an end-to-end churn prediction system on Google Cloud that identifies at-risk SaaS customers before they cancel, enabling proactive retention campaigns that measurably reduce monthly churn and protect recurring revenue.
Manufacturing Defect Detection
Build an end-to-end computer vision pipeline on Google Cloud that detects surface defects in real time on manufacturing production lines — from image capture at the edge to AutoML Vision training, custom CNN fine-tuning, and low-latency edge deployment with Vertex AI and Edge TPU.
Product Recommendation Engine
Build a production-grade hybrid recommendation system on Google Cloud Platform. Combine collaborative filtering via BigQuery ML Matrix Factorization with content-based embeddings, serve predictions through Vertex AI endpoints, and measure impact with A/B testing — turning generic product