Vertex AI: Google's AI and ML Platform

Vertex AI is Google’s unified AI and machine learning platform that brings generative AI, classic ML, and MLOps together under one consistent API and UI. It supports the entire workflow from data preparation through training and deployment to monitoring and running AI agents in production, including direct access to Google’s Gemini models. Google now markets Vertex AI as the Gemini Enterprise Agent Platform.

What is Google Vertex AI?

Vertex AI brings all of Google Cloud’s AI and ML services together under a consistent API and user interface. The platform covers the entire lifecycle: from data preparation through training and deployment to monitoring, retraining, and operating AI agents. This consolidates the fragmented landscape of the previous AI Platform and provides an end-to-end workflow for data scientists, ML engineers, and application teams.

A key feature is the integration of Google’s Gemini models. The current generation includes Gemini 2.5 Pro for advanced reasoning and coding with up to 1 million tokens of context, Gemini 2.5 Flash for latency-sensitive applications, and Gemini 2.5 Flash-Lite for high volumes at low cost. These are joined by Imagen for image generation, Veo for video, and Gemini Embedding for vector representations. The earlier PaLM and Codey APIs have been retired and replaced by the Gemini family. Model Garden additionally provides access to over 200 models from Google and partners, including Anthropic Claude, Meta Llama, Mistral, and Google’s open Gemma models.

Vertex AI distinguishes between AutoML for automated machine learning without code and Custom Training for full control over model architecture and training logic. Vertex AI Workbench and Colab Enterprise provide managed notebook environments, while Vertex Pipelines orchestrates MLOps workflows. The BigQuery-based Feature Store enables central feature management, and tools like Model Monitoring and Explainable AI detect model drift and performance degradation. With Agent Builder and Agent Engine you develop and run AI agents with grounding on your own data and tool use. Training can be performed on CPUs, GPUs, or Cloud TPUs, with support for common frameworks like TensorFlow, PyTorch, JAX, and scikit-learn.

The service bills based on usage: Gemini models by tokens, training and predictions by compute. For predictable workloads there is Provisioned Throughput, batch processing, and context caching. Vertex AI is available in multiple EU regions with GDPR compliance and EU data residency. SLA: 99.9% for online prediction endpoints.

Vertex AI Comparison

vs. AWS SageMaker: Vertex AI offers direct access to Google Foundation Models like Gemini, while SageMaker focuses more on the AWS ecosystem. Vertex AI has simpler pricing models and better BigQuery integration for data analytics.

vs. Azure Machine Learning: Vertex AI excels with TPU availability and Google’s expertise in large-scale ML training. Azure has better integration into Microsoft ecosystems, while Vertex AI shows stronger open-source orientation.

vs. STACKIT AI Model Serving: STACKIT offers German data sovereignty and local data centers, while Vertex AI delivers a broader range of Foundation Models and global availability.

Core Features

• Gemini and Model Garden: Direct access to Gemini 2.5 Pro, Flash, and Flash-Lite plus over 200 additional models, including Imagen, Veo, Gemma, Anthropic Claude, Meta Llama, and Mistral.
• AI agents: Agent Builder, Agent Engine, and Agent Garden for developing, testing, and operating agents with grounding, tool use, and code execution in isolated sandboxes.
• MLOps: Vertex Pipelines, Model Registry, experiment tracking, and metadata management for reproducible and automated ML workflows.
• Training and tuning: No-code AutoML, Custom Training with full control, and supervised fine-tuning for Gemini, on CPUs, GPUs, and Cloud TPUs.
• Feature Store and data: BigQuery-based Feature Store with Bigtable online serving for consistent features between training and serving.
• Monitoring and governance: Model Monitoring for drift, Explainable AI for feature attributions, and evaluation tools for ongoing quality assurance.

Common Use Cases

LLM Fine-Tuning with Gemini for Customer Service

An e-commerce company uses Vertex AI to fine-tune Gemini 2.5 Flash with supervised fine-tuning on proprietary product data and customer interactions. The model answers product-specific questions more precisely than generic LLMs. Via Vertex AI Pipelines, the model is retrained weekly with new data, while Model Monitoring tracks answer quality.

Computer Vision for Retail Quality Control

A manufacturer deploys AutoML Vision for automated quality inspection in production. Thousands of product images per hour are analyzed, defects detected in real-time. The system was trained in four weeks with AutoML, without ML expertise. Batch Predictions process historical data for trend analysis.

Demand Forecasting with Custom Training

A retail chain uses Custom Training with XGBoost on Vertex AI for precise demand forecasting. The Feature Store centralizes features like weather data, holidays, and historical sales. Vertex Pipelines orchestrates daily retraining, Online Predictions deliver forecasts to ordering systems in under 100ms.

Fraud Detection with Real-Time Predictions

A bank deploys a fraud detection model on Vertex AI with Online Predictions. Transactions are evaluated in real-time, suspicious activities blocked immediately. Model Monitoring detects new fraud patterns, triggers automatic retraining. The solution processes 50,000 transactions per second with p99 latency under 20ms.

Personalized Recommendation Engine

A streaming platform uses Vertex AI for personalized content recommendations. The Feature Store stores user features and content embeddings, a Custom Training model generates recommendations. Vertex Explainable AI shows which features influence recommendations. A/B tests via Vertex Experiments continuously optimize the model.

Document Processing with Document AI Integration

An insurer combines Vertex AI with Document AI for automated claims processing. Document AI extracts data from forms, a Custom Classification Model on Vertex AI categorizes claim types. Vertex Pipelines orchestrates the entire workflow from upload to decision, reducing processing time by 70%.

Multi-Cloud MLOps with Vertex Pipelines

A technology company uses Vertex Pipelines for reproducible ML workflows across multiple teams. Pipelines orchestrate data validation, training, evaluation, and deployment. Metadata tracking documents every run, Model Registry manages versions. The setup reduces time-to-production from months to weeks.

Best Practices for Vertex AI

Choosing AutoML vs. Custom Training Correctly

AutoML is suitable for quick prototypes, standard tasks like image classification or tabular data, and teams without ML expertise. Custom Training is necessary for complex architectures, special loss functions, existing TensorFlow/PyTorch models, or when you need full control over hyperparameters. Use AutoML for initial baselines, migrate to Custom Training when customizations become necessary.

Establishing MLOps with Vertex Pipelines

Vertex Pipelines orchestrates reproducible ML workflows with Kubeflow Pipelines or TFX. Define pipelines as code, version them in Git. Automate data validation, training, evaluation, and deployment in one pipeline. Use conditional steps for A/B tests and rollback mechanisms. Pipeline templates reduce boilerplate for recurring workflows.

Model Monitoring and Automatic Retraining

Configure Model Monitoring for prediction drift and training-serving skew from the first deployment. Set alerting thresholds based on business metrics, not just ML metrics. Implement automatic retraining pipelines that trigger on drift detection. Use shadow deployments for new model versions before production rollout.

Strategic Use of Feature Store

The Vertex AI Feature Store centralizes features and avoids redundant feature engineering across teams. Define features once, use them for training and serving. Version features to ensure consistency between historical and current data. Use online serving for low-latency predictions and offline serving for batch jobs and training.

Efficient Hyperparameter Tuning

Vertex AI offers Vizier for Bayesian optimization in hyperparameter search. Define meaningful search spaces based on domain knowledge, avoid overly broad ranges. Use parallel trials for faster convergence, but consider compute costs. Early stopping reduces resource waste on unsuccessful trials. For exploratory searches, use random search before grid search.

Cost Optimization with Preemptible VMs and Batch Predictions

Preemptible VMs reduce training costs by up to 80%, but are only suitable for fault-tolerant workloads. Implement checkpointing for preemptible training jobs. Use Batch Predictions instead of Online Predictions when real-time is not necessary, costs per prediction are significantly lower. Choose smaller machine types for predictions when possible, scale only when needed.

Implementing Responsible AI Practices

Use Vertex Explainable AI to make model decisions transparent. Feature attributions show which inputs influence predictions. Test models for bias across different demographic groups with What-If-Tool. Implement fairness metrics in model evaluation. Document model behavior and limitations in Model Cards for stakeholder transparency.

Benefits

• One platform for AI and ML: Generative AI, classic ML, MLOps, and AI agents work together under one API instead of being spread across separate tools.
• Access to leading models: Gemini and over 200 models in Model Garden give you the choice between Google, partner, and open-source models without switching providers.
• GDPR and EU data residency: Multiple EU regions with processing and storage in European data centers meet strict compliance requirements.
• Flexible cost models: Usage-based billing, Provisioned Throughput for predictable workloads, batch, and context caching reduce costs in a targeted way.
• Tight Google Cloud integration: Native connections to BigQuery, Cloud Storage, and Document AI accelerate data flow and production applications.

Integration with innFactory

As a Google Cloud partner, innFactory supports you with Vertex AI: architecture design, migration of existing ML workloads, building AI agents, MLOps setup, cost optimization, and team enablement.

Contact us for a consultation on Vertex AI and Google Cloud.