Directory AI & ML PlatformsML Platforms & MLOpsVertex AI Pipelines

Vertex AI Pipelines

About Vertex AI Pipelines

It runs ML pipelines defined with either the Kubeflow Pipelines or TensorFlow Extended (TFX) framework. An ML pipeline is described as a portable and extensible description of an MLOps workflow made up of steps called pipeline tasks. Each pipeline task performs a specific step in the workflow to train and deploy a model. The pipeline structure is a directed acyclic graph of containerized tasks connected by input-output dependencies. Pipeline definitions can be reused to continuously retrain a model on the latest production data.

Products

Google Cloud Pipeline Components: Predefined components used to define pipeline tasks, including features such as AutoML.

Used for

Automating and monitoring repeatable processes in ML practice.
Retraining a model continuously on the latest production data.
Preparing or preprocessing training data.
Training a model using prepared training data.
Deploying a trained model for predictions.

From Vertex AI Pipelines's own website, read 2026-10-24. How we build listings.

How to evaluate ML Platforms & MLOps

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Vertex AI Pipelines. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.

AI/ML Platforms

25%

MLOps, Serving & Lifecycle

Reproducible training pipelines and CI/CD for models, a first-class model registry with versioning and stage promotion, real-time and batch/streaming serving, A/B and canary rollout, automated retraining, and rollback — the path from registered model to monitored endpoint and back

20%

Model Development & Experimentation

Notebook and IDE experience, experiment tracking and run comparison, feature engineering and a feature store, AutoML for breadth, distributed training, and support for the frameworks your teams use (PyTorch, TensorFlow, scikit-learn, XGBoost, Spark ML)

20%

AI Governance & Responsible AI

End-to-end lineage from data to deployed model, approval workflows and access control, bias and fairness testing, explainability, model and prompt audit trails, and the evidence trail needed for internal risk review and emerging AI regulation such as the EU AI Act

15%

GenAI & LLM Operations

Managed serving and fine-tuning of open and proprietary LLMs, a vector/retrieval layer for RAG, prompt management, evaluation harnesses for non-deterministic output, agent orchestration hooks, and token and inference cost controls — weighted to how central GenAI is to you

10%

Data, Compute & Cost Control

Proximity to where your data already lives and the egress it avoids, GPU availability and scheduling, spot/preemptible support, autoscaling, idle-resource reclamation, and chargeback/showback visibility so AI spend is attributable and governable rather than a surprise

10%

Platform Openness & Ecosystem

Portability and exit cost (open formats, MLflow compatibility, container-based serving), multi-cloud and on-prem reach, breadth of integrations, collaboration across data science and engineering, and standards conformance so the platform extends rather than confines your stack

MLOps Platforms

25%

Production Monitoring & Reliability

Data and concept drift detection, prediction/quality monitoring, ground-truth join for delayed labels, alerting and automated retraining triggers, plus LLM-specific signals (hallucination, toxicity, response-quality eval) on live traffic

20%

Model Registry & Governance

Versioned registry with stage transitions and approval gates, end-to-end lineage (data → run → model → endpoint), reproducibility, model cards, audit logging, and alignment to your AI risk/inventory policy

20%

Deployment & Serving

One-step deploy to real-time and batch endpoints, autoscaling and GPU-aware serving, canary / shadow / A-B rollout, rollback, multi-model and multi-framework support, and latency/throughput under your traffic

15%

CI/CD & Pipeline Automation

Git-native pipelines, reproducible training/eval/deploy stages, integration with your existing CI (Actions, GitLab, Azure DevOps), environment/dependency management, and IaC-friendly APIs

10%

Experiment Tracking & Reproducibility

Run logging at scale, metric/artifact comparison, hyperparameter sweeps, dataset and code versioning, collaboration, and a durable system-of-record across teams and frameworks

10%

LLMOps Coverage

Prompt and version management, eval datasets and LLM-as-judge scoring, RAG/agent tracing, online evaluation in production, and a path to manage classical models and LLMs through one stack

Other ML Platforms & MLOps Vendors

Azure Machine Learning

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

cloud.google.com/vertex-ai/docs/pipelines/introduction

CategoryAI & ML Platforms

SubcategoryML Platforms & MLOps

DeploymentServerless

We publish a detail only when we can point at the page it came from. Know when Vertex AI Pipelines was founded or where it is based? Send it to us, free.