Data Engineering Project | Build a Real-Time Prediction ML Pipeline in 60 Minutes using Google cloud скачать в хорошем качестве

Data Engineering Project | Build a Real-Time Prediction ML Pipeline in 60 Minutes using Google cloud

This project showcases a real-time reorder prediction pipeline built entirely on Google Cloud Platform (GCP), simulating how modern e-commerce systems can predict repeat purchases as events occur. The system begins with a Python script (simulate_orders.py) that continuously publishes synthetic user-product interactions to a Pub/Sub topic, emulating real-time order events. Each event includes a user_id, product_id, and a timestamp. Google Cloud Dataflow, powered by Apache Beam, consumes these messages from the Pub/Sub topic. The streaming pipeline (reorder_dataflow.py) processes each event, enriches it with additional logic (such as scoring), and writes the output into a BigQuery table called reorder_predictions. In production scenarios, this logic can include real-time calls to a BigQuery ML model trained on historical features (user_item_features), such as order frequency, average cart position, and recency. A separate BigQuery ML model (reorder_model) is trained using these historical patterns to predict the likelihood of a reorder. This enables the pipeline to simulate intelligent, data-driven decisions in real-time environments. The project enforces access control using appropriate IAM roles. These permissions ensure secure and seamless integration across GCP services. This project serves as a practical demonstration of real-time machine learning inference and cloud-native stream processing. It is ideal for academic demonstrations, cloud engineering practice, or prototyping real-world data products that rely on event-driven architecture. Let me know if you'd like a visual summary or one-line abstract as well! Dataset : https://www.kaggle.com/datasets/instacart/... Github Link: https://github.com/Insightfultechnotes/Rea... Ask ChatGPT