A Practical Guide to Operationalizing Machine Learning on Google Cloud
This whitepaper, authored by a Royal Cyber AI/ML engineer, provides a hands-on, implementation-focused guide to building MLOps pipelines on Google Cloud Vertex AI—with a bonus: integrating GenAI for conversational model interaction.
MLOps Fundamentals, Demystified
Cut through the complex diagrams. Learn the core components: Data → Training → Evaluation → Deployment. Then understand what really matters—automating the pieces that change.
Hybrid Open-Source + Managed Cloud Architecture
Combine Kubeflow Pipelines (open source) with Vertex AI (managed). Build components from scratch, not just prebuilt blocks. Retain flexibility without sacrificing scalability.
CI/CD for Machine Learning
Automate container builds and pipeline execution with Google Cloud Build. Trigger retraining on every code commit. No manual steps.
Train on BigQuery data using CatBoost—a high-performance gradient boosting library. Package training code as reusable, containerized Kubeflow components.
Model Deployment with FastAPI + Vertex Endpoints
Serve predictions via FastAPI and Uvicorn. Upload models to Vertex Model Registry. Deploy to managed Vertex Endpoints. Version, monitor, and scale.
Pipeline Observability
Track parameters, metrics, and artifacts at every step. Vertex AI provides complete lineage and transparency across training and inference.
Bonus: Generative AI + MLOps
Add a conversational interface to your deployed model. Let users interact with predictions using natural language. A glimpse into the next generation of AI/ML interfaces.
Who Should Read This
ML Engineers & Data Scientists – Move from notebooks to production pipelines
MLOps Platform Teams – Implement CI/CD and automation on Vertex AI
Technical Architects – Design hybrid open-source + managed cloud ML systems
AI/ML Leaders – Understand practical MLOps implementation beyond slideware
GenAI Practitioners – Learn how generative interfaces can augment traditional ML
What You'll Build
- Ingests data from BigQuery
- Trains a CatBoost model in a custom container
- Registers the model in Vertex Model Registry
- Deploys to a Vertex Endpoint with FastAPI serving
- Automates everything via Cloud Build triggers on code commits
- Adds a conversational GenAI layer for natural language predictions
Includes working code, Cloud Build configuration, pipeline definitions, and deployment patterns you can adapt immediately.