Vertex AI in Practice: From Training Pipelines to Production Endpoints (Plus Where Gemini Fits)

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Google Cloud Vertex AI is a unified platform for building, training, deploying, and operating ML systems—covering both predictive ML (your own models) and generative AI (Gemini and other foundation models) under the same governance, IAM, and observability umbrella. (Google Cloud)

This post walks through a practical, production-minded path:

  • Train models using Vertex AI Training Pipelines

  • Optimize with hyperparameter tuning

  • Govern and version with Model Registry

  • Automate workflows with Vertex AI Pipelines (KFP v2)

  • Deploy to endpoints for online inference

  • Add a Gemini capability when you need GenAI

1) The core Vertex AI building blocks you’ll use in real projects

Training pipelines (custom training, managed infrastructure)

Vertex AI training pipelines let you run custom training on managed compute and automatically create a Model resource from your outputs. This is a big deal for MLOps because the “trained artifact → registered model” step is part of the platform flow, not an afterthought. (Google Cloud Documentation)

Hyperparameter tuning (HPT)

Hyperparameter tuning in Vertex AI runs many trials across different configurations using Google Cloud infrastructure—helpful for squeezing out accuracy improvements without writing your own tuning orchestration. (Google Cloud Documentation)

Model Registry (versioning + lifecycle)

Vertex AI Model Registry is your central system of record: organize models, track versions, and deploy versions to endpoints (including via aliases). It also supports registering BigQuery ML models alongside other models, which is handy if teams mix “SQL ML” and Python ML. (Google Cloud Documentation)

Vertex AI Pipelines (workflow automation, lineage, reproducibility)

Vertex AI Pipelines supports Kubeflow Pipelines (KFP) SDK v2+, and you can build workflows using prebuilt Google Cloud Pipeline Components (for training jobs, dataset ops, batch prediction, etc.). (Google Cloud Documentation)

Also: pipeline runs can be associated with Vertex AI Experiments to compare variables like hyperparameters and training environments over time. (Google Cloud Documentation)

Endpoints (online inference)

For low-latency predictions, you deploy a model to a Vertex AI endpoint using the console, CLI, or API/SDK. (Google Cloud Documentation)

2) A production-grade workflow (what “good” looks like)

Here’s a clean pattern most real teams converge on:

  1. Ingest + prep data (often BigQuery → export/transform → GCS artifacts)

  2. Train via a Vertex AI Training Pipeline (custom job or AutoML)

  3. Tune hyperparameters (optional but common)

  4. Evaluate (offline metrics + slice checks)

  5. Register the winning model in Model Registry (new version)

  6. Deploy to a Vertex AI endpoint (or run batch prediction)

  7. Automate retraining + evaluation with Vertex AI Pipelines

  8. Track & govern with experiments, labels, and consistent regions/projects

The key is repeatability: every run should leave behind traceable artifacts (data snapshot, code/container version, parameters, metrics, model version).

3) Reference implementation: a minimal Vertex AI Pipeline (KFP v2)

Below is a simplified sketch (not a full project) showing how teams structure continuous training. The official tutorial pattern commonly uses components like:

  • CustomTrainingJobOp to run training

  • ModelUploadOp to upload trained artifacts into the platform/model registry

  • (Optionally) ModelBatchPredictOp and evaluation components in the same workflow (Google Cloud Documentation)

Install the typical pipeline toolchain

pip install google-cloud-aiplatform kfp google-cloud-pipeline-components

A “train → upload model” pipeline skeleton

from kfp import dsl, compiler
from google.cloud import aiplatform
from google_cloud_pipeline_components import aiplatform as gcc_aip

PROJECT_ID = "your-project"
REGION = "us-central1"
BUCKET_URI = "gs://your-staging-bucket"

aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)

@dsl.pipeline(name="vertex-train-and-register")
def pipeline(
    display_name: str = "sklearn-train",
    model_display_name: str = "churn-model",
):
    # 1) Run training as a managed Vertex AI Custom Job
    train_task = gcc_aip.CustomTrainingJobOp(
        project=PROJECT_ID,
        location=REGION,
        display_name=display_name,
        worker_pool_specs=[{
            "machine_spec": {"machine_type": "e2-standard-4"},
            "replica_count": 1,
            "python_package_spec": {
                # Example: use a prebuilt training image + your packaged trainer
                "executor_image_uri": "us-docker.pkg.dev/vertex-ai/training/sklearn-cpu.1-0:latest",
                "package_uris": [f"{BUCKET_URI}/trainer-0.1.tar.gz"],
                "python_module": "trainer.task",
                "args": ["--some_arg", "some_value"],
            },
        }],
    )

    # 2) Upload outputs to Vertex AI as a Model resource (shows up in Model Registry)
    gcc_aip.ModelUploadOp(
        project=PROJECT_ID,
        location=REGION,
        display_name=model_display_name,
        artifact_uri=train_task.outputs["gcs_output_directory"],
        serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.1-0:latest",
    )

compiler.Compiler().compile(pipeline_func=pipeline, package_path="train_and_register.yaml")

Why this structure works:

  • Training is fully managed (repeatable infra + logs)

  • The pipeline produces a first-class Model artifact you can version and deploy (Google Cloud Documentation)

  • You can expand it with evaluation, conditional deployment, notifications, and scheduled triggers (common in continuous training setups) (Google Cloud Documentation)

4) Deploying the model to an endpoint (online inference)

After you have a model version in Model Registry, you typically deploy it to an endpoint for real-time inference. Deployment can be done through the console, CLI, or SDK/API. (Google Cloud Documentation)

Operational tips:

  • Keep training, registry, endpoints in the same region unless you have a strong reason not to (it reduces friction and surprises).

  • Use consistent labels across pipeline runs for cost attribution and traceability (Vertex AI Pipelines supports label propagation to many created resources). (Google Cloud Documentation)

5) Where Gemini (Generative AI) fits inside Vertex AI

Many teams now ship systems that combine:

  • A predictive model (fraud score, churn risk, anomaly detection)

  • A Gemini-powered layer (explanations, summarization, chat/agent UX)

Vertex AI exposes Gemini models and GenAI features, and you can develop/test prompts in Vertex AI Studio. (Google Cloud Documentation)

A minimal Python example (Gemini via Vertex AI SDK):

from vertexai.generative_models import GenerativeModel
import vertexai

vertexai.init(project="your-project", location="us-central1")

model = GenerativeModel("gemini-2.5-flash")
resp = model.generate_content("Write a 3-bullet summary of model monitoring best practices.")
print(resp.text)

The GenerativeModel API supports generate_content and related options (config, safety settings, tool calling, etc.). (Google Cloud Documentation)

If you’re starting from scratch, Google also maintains quickstarts and code sample collections for the Gemini API on Vertex AI. (Google Cloud Documentation)

6) Practical “don’t regret it later” best practices

  • Treat Model Registry like Git for models: every meaningful change becomes a new version; use descriptions/labels to encode what changed. (Google Cloud Documentation)

  • Automate with pipelines early: even a simple “train → evaluate → register” pipeline saves you from notebook-driven deployments. (Google Cloud Documentation)

  • Use experiments to compare runs: makes tuning and iteration far less chaotic. (Google Cloud Documentation)

  • Know when to do batch vs online: endpoints are for low latency; batch prediction is for large offline scoring jobs (often cheaper/easier to operate at scale). (Google Cloud Documentation)

  • GenAI endpoints/locations matter: GenAI features on Vertex AI are exposed through specific regional/global endpoints—plan for availability, data residency, and latency. (Google Cloud Documentation)

Wrap-up

Vertex AI shines when you stop thinking of it as “a place to train a model” and start using it as a full MLOps + GenAI platform:

  • Training pipelines to standardize training

  • Hyperparameter tuning to optimize

  • Model Registry for lifecycle + versioning

  • Pipelines (KFP v2) for automation and reproducibility

  • Endpoints for production inference

  • Gemini models when you need generative capabilities

If you want, paste your target use case (e.g., tabular classification, image model, RAG chatbot, or an “ML + Gemini” hybrid) and I’ll tailor the architecture + a runnable sample repo structure (folders, Dockerfiles, pipeline YAML compile step, and deployment flow).

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