ML Pipeline Architecture

Production-grade machine learning systems that scale.

The Problem

Most ML projects die in notebooks. Models that work locally fail in production. Data pipelines break silently. Teams waste months rebuilding infrastructure instead of shipping features.

The Solution

I design and build end-to-end ML pipelines on GCP that actually work:

✓ Data ingestion from any source (APIs, databases, streaming)
✓ Feature engineering with proper versioning
✓ Model training with experiment tracking
✓ Automated retraining and monitoring
✓ Serving infrastructure (batch or real-time)

How It Works

Discovery Call

Understand your data, models, and business goals

Architecture Design

Blueprint the pipeline with clear milestones

Build & Deploy

Implement on GCP (BigQuery, Vertex AI, Cloud Functions)

Handoff & Documentation

Your team owns it, fully documented

Tech Stack

Google Cloud Platform BigQuery Vertex AI Cloud Functions Cloud Run Python pandas scikit-learn TensorFlow/PyTorch Airflow / Cloud Workflows MLflow Terraform

Check out existing posts on the blog

Ready to ship ML that works?

Book a System Design Review