Vertex AI MLOps & Model Evaluation Framework

A controlled Google Cloud MLOps loop for repeatable model training, evaluation, artifact handling, telemetry, and promotion decisions.

Anonymized architecture case study

Architecture diagram for a Vertex AI MLOps and model evaluation framework — Architecture view with client and environment identifiers removed.

Case Study Overview

A fine-tuned Gemini workflow needed repeatable training and evaluation, retained pipeline artifacts, centralized operational signals, and a traceable path from source change to model-quality review.

Outcome

A governed MLOps framework that helps teams inspect model quality and platform behaviour before promotion and supports more deliberate release decisions.

Architecture Approach

1Trigger repeatable workflows through Cloud Build, Cloud Scheduler, and Vertex AI Pipelines.
2Retain evaluation datasets and pipeline artifacts in Cloud Storage rather than treating runs as ephemeral.
3Send logs and evaluation outputs to Cloud Logging and BigQuery for inspection and comparison.
4Keep model tuning, evaluation, monitoring, and promotion signals connected in one architecture.

Role & Scope

MLOps architecture, evaluation workflow, and observability design

Technology Stack

Google Cloud

Gemini

Vertex AI

Vertex AI Pipelines

Cloud Build

Cloud Scheduler

Cloud Storage

Cloud Logging

BigQuery

Model Evaluation

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