Vertex AI

Vertex AI is Google Cloud’s unified machine learning platform that brings together all Google Cloud services for building ML under one unified UI and API. It supports the entire ML workflow from data preparation to model deployment and monitoring, including access to Google’s foundation models.

What is Google Vertex AI?

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

A key feature is the integration of Google Foundation Models such as Gemini for multimodal applications, PaLM 2 for text processing, and Imagen for image generation. These models can be used directly via API, fine-tuned with custom data, or used as a basis for specialized applications. Model Garden additionally provides access to hundreds of pre-trained models from Google and partners that can be deployed without extensive training.

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 provides a managed Jupyter environment, while Vertex Pipelines orchestrates MLOps workflows. The Feature Store enables central feature management, and comprehensive monitoring tools automatically detect model drift and performance degradation. Training can be performed on CPUs, GPUs, or TPUs, with support for all common frameworks like TensorFlow, PyTorch, and scikit-learn.

The service offers pay-per-use billing for training, predictions, and API calls in multiple EU regions with GDPR compliance. SLA: 99.9% for 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.

Common Use Cases

LLM Fine-Tuning with Gemini for Customer Service

An e-commerce company uses Vertex AI to fine-tune Gemini 1.5 Pro with 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.

Integration with innFactory

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

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