Learn how to build and deploy a complete machine learning pipeline on Google Cloud — from data prep to prediction API — using scalable GCP services.
Most ML tutorials stop at model training in a notebook.
But in the real world, scalability, reliability, and deployment matter.
Google Cloud Platform (GCP) isn’t just infrastructure — it’s a robust, ML-optimized ecosystem where you can go from CSV to real-time predictions without reinventing the wheel.
This post is your end-to-end guide to building a production-grade ML pipeline on GCP using:
✅ Cloud Storage
✅ BigQuery
✅ Vertex AI
✅ Cloud Functions
✅ Cloud Scheduler
✅ and more
Let’s break the pipeline into digestible stages:
- Data Ingestion → Google Cloud Storage…