Google Vertex AI: Unified ML Platform on GCP

Q: Do I use Vertex AI to run open-source models or foundation models?

Both. Via custom training, you train and deploy your own models (including open-source architectures from Hugging Face/PyTorch). Via Model Garden, you access foundation models — Google's Gemini and select third-party/open models — to prompt, fine-tune, or ground. It spans "run my own model" and "use a powerful pre-trained model," unlike Hugging Face (open-weights focus) or a pure hosted-API.

Q: Is Vertex AI only for ML experts?

No — it spans levels. AutoML lets teams with limited ML expertise train quality models from labeled data. Foundation models let developers use powerful pre-trained models (Gemini) via prompting or light tuning without deep ML knowledge. Custom training serves ML experts needing full control. The platform meets you at your expertise level, though production MLOps benefits from ML/ops maturity regardless.

Google Vertex AI

Google Vertex AI is GCP's unified machine learning platform — a single environment for the entire ML lifecycle: preparing data, training models, tuning them, deploying them to production endpoints, and monitoring and managing them over time (MLOps). It brings together what used to be scattered GCP ML services into one platform, and increasingly serves as the gateway to Google's foundation models (the Gemini family) and generative AI capabilities alongside custom model training.

Vertex AI is Google's answer to AWS SageMaker and Azure Machine Learning — a managed platform that removes the infrastructure toil of doing ML at scale (provisioning training clusters, serving infrastructure, pipeline orchestration) so data science and ML teams focus on models. It spans two audiences: teams building custom models (train with PyTorch/TensorFlow/scikit-learn, deploy, monitor) and teams using foundation models (call Gemini, tune it, ground it on their data). The ML and MLOps concepts apply; this is the managed GCP platform.

TL;DR

Vertex AI is GCP's unified ML platform — data prep, training, tuning, deployment, and monitoring in one place (GCP's SageMaker equivalent).
Two broad uses: custom models (bring your PyTorch/TensorFlow/scikit-learn code, train on managed infrastructure, deploy to endpoints) and foundation models (access Gemini and other models via the Model Garden, tune and ground them).
MLOps tooling: Pipelines (orchestrate ML workflows), Feature Store (manage features), Model Registry (version models), Experiments (track runs), and monitoring (detect drift).
AutoML trains quality models from your data with minimal ML expertise; custom training gives full control.
Removes infrastructure toil — no managing training clusters or serving infrastructure yourself — so teams focus on models, not ops.
The generative-AI gateway on GCP: call, tune, and ground Gemini and other foundation models (Google's hosted-model counterpart to running open weights).

Core Concepts

The Unified Lifecycle

Vertex AI covers the full ML lifecycle in one platform:

Before Vertex AI, these steps used separate GCP services; unifying them means one place for data scientists to train, deploy, and manage models with consistent tooling — the value proposition of a managed ML platform.

Custom Models vs AutoML vs Foundation Models

Vertex AI serves different levels of ML need:

Custom training gives full control (your framework, your architecture) on managed infrastructure. AutoML democratizes ML — feed it labeled data, get a deployable model. Foundation models (via Model Garden) provide access to Google's Gemini and other pre-trained models for generative AI, which you can prompt, fine-tune, or ground on your data — GCP's path to using large language models.

MLOps Tooling

Vertex AI's MLOps features address the hard part of ML — operating models in production:

Pipelines — orchestrate reproducible ML workflows (data prep → train → evaluate → deploy) as DAGs, similar to Airflow but ML-specialized (built on Kubeflow Pipelines).
Feature Store — centrally manage, serve, and reuse features across models, ensuring training/serving consistency.
Model Registry — version and govern models.
Experiments — track training runs, parameters, and metrics for reproducibility and comparison.
Model Monitoring — detect data drift and training-serving skew in production, alerting when a model degrades.

These turn ad-hoc notebook ML into governed, reproducible, monitored production systems — the essence of MLOps.

Deployment and Serving

Trained models deploy to endpoints — managed, autoscaling serving infrastructure that exposes the model as an API for online (real-time) predictions, or batch prediction for offline scoring of large datasets. Vertex handles the serving infrastructure (scaling, load balancing), so you don't build and operate a model-serving stack yourself — the deployment half of MLOps.

Common Mistakes

Using the Full Platform for a Simple Model

Vertex AI is powerful but has a learning curve and cost. For a simple model or prototype, a notebook or lightweight serving may suffice — the full platform (Pipelines, Feature Store, Registry) pays off for production ML at scale with multiple models and teams. Adopt the pieces you need; don't impose the whole MLOps apparatus on a one-off model.

Skipping Model Monitoring

Deploying a model and never monitoring it means silent degradation — data drifts, the world changes, and predictions get worse without anyone noticing. Use Model Monitoring to detect drift and skew, and plan retraining. A deployed-but-unmonitored model is a common MLOps failure.

Training-Serving Skew

Computing features differently in training vs serving produces subtly wrong predictions in production. The Feature Store exists partly to prevent this — serve the same feature definitions in both. Ignoring skew is a classic ML production bug (see feature engineering).

Overspending on Training/Serving Compute

GPU/TPU training and always-on endpoints are expensive. Right-size training resources, use batch prediction (not always-on endpoints) for offline scoring, autoscale endpoints, and shut down idle notebooks/resources. ML compute is a major cost line — govern it.

Reaching for Custom Training When AutoML or Foundation Models Fit

Building and training a custom model when AutoML would produce a good result faster, or when a foundation model (Gemini) prompted or lightly tuned would solve the problem, wastes effort. Match the approach to the need: foundation models for generative/complex language tasks, AutoML for standard predictive tasks, custom training when you genuinely need bespoke models.

FAQ

What is Vertex AI in one sentence?

GCP's unified platform for the entire machine-learning lifecycle — preparing data, training and tuning models (custom, AutoML, or foundation models like Gemini), deploying them to managed endpoints, and monitoring them in production — so ML teams focus on models rather than infrastructure. It's Google's SageMaker/Azure ML equivalent.

Vertex AI vs AWS SageMaker vs Azure ML?

All three are managed, end-to-end ML platforms covering training, deployment, and MLOps — the same category on different clouds. Vertex AI's distinctive strengths are tight integration with GCP data services (BigQuery, Cloud Storage) and access to Google's Gemini foundation models. Choose by cloud; the ML platform concepts (pipelines, feature stores, model registries, monitoring) transfer across all three.

Do I use Vertex AI to run open-source models or foundation models?

Both. Via custom training, you train and deploy your own models (including open-source architectures from Hugging Face/PyTorch). Via Model Garden, you access foundation models — Google's Gemini and select third-party/open models — to prompt, fine-tune, or ground. It spans "run my own model" and "use a powerful pre-trained model," unlike Hugging Face (open-weights focus) or a pure hosted-API.

How does Vertex AI help with MLOps?

Through Pipelines (orchestrate reproducible workflows), Feature Store (consistent feature management), Model Registry (versioning/governance), Experiments (track runs), and Model Monitoring (detect drift/skew). Together these turn ad-hoc model-building into governed, reproducible, monitored production ML — addressing the operational challenges the MLOps discipline targets. It's the "operate models in production reliably" half of the platform.

Is Vertex AI only for ML experts?

No — it spans levels. AutoML lets teams with limited ML expertise train quality models from labeled data. Foundation models let developers use powerful pre-trained models (Gemini) via prompting or light tuning without deep ML knowledge. Custom training serves ML experts needing full control. The platform meets you at your expertise level, though production MLOps benefits from ML/ops maturity regardless.