Google Vertex AI

What is Google Vertex AI?

Google Vertex AI is Google Cloud's unified AI and machine learning platform that brings together all the tools developers and data scientists need to build, train, and deploy AI models at scale. Launched in May 2021 as the successor to Google's AI Platform, Vertex AI consolidates previously separate services (AutoML, AI Platform Training, AI Platform Prediction) into a single, cohesive environment. The platform serves as Google Cloud's central hub for AI development, providing access to pre-trained models, custom training infrastructure, automated machine learning (AutoML), and enterprise-grade deployment capabilities.

Vertex AI supports both no-code/low-code workflows through AutoML and advanced custom development using popular frameworks like TensorFlow, PyTorch, and scikit-learn. The platform provides access to Google's foundation models including Gemini (multimodal AI), PaLM 2 (language understanding), Imagen (image generation), and Chirp (speech recognition). Beyond model access, Vertex AI offers comprehensive MLOps capabilities including model monitoring, versioning, A/B testing, and automated retraining—enabling teams to manage the full machine learning lifecycle from experimentation to production.

Core Components and Features

Model Garden and Foundation Models

• Gemini models - Google's most capable multimodal AI (text, image, video, audio)
• PaLM 2 - Advanced language models for text generation and understanding
• Imagen - State-of-the-art text-to-image generation
• Chirp - Universal speech model supporting 100+ languages
• Codey - Code generation and completion models
• Access to 100+ open-source models (Llama, Stable Diffusion, BERT)
• Model fine-tuning and customization on proprietary data
• One-click model deployment with managed infrastructure

AutoML - No-Code Machine Learning

• AutoML Vision - Image classification, object detection, segmentation
• AutoML Natural Language - Text classification, entity extraction, sentiment analysis
• AutoML Tables - Structured data prediction (regression, classification)
• AutoML Video - Video classification and action recognition
• Automated neural architecture search (NAS) for optimal model design
• Hyperparameter tuning with Google's Vizier optimization
• Explainable AI to understand model predictions
• No coding required - upload data, select target, deploy model

Custom Training and Development

• Support for TensorFlow, PyTorch, scikit-learn, XGBoost, JAX
• Pre-built training containers for popular frameworks
• Custom container support for any ML framework or library
• Distributed training on multiple GPUs/TPUs
• Hyperparameter tuning at scale with parallel trials
• Training on NVIDIA GPUs (A100, V100, T4) or Google TPUs
• Managed Jupyter notebooks with pre-configured environments
• Integration with BigQuery for large-scale data processing

MLOps and Production Deployment

• Vertex AI Pipelines - Orchestrate ML workflows with Kubeflow or TFX
• Model Registry - Centralized model versioning and lineage tracking
• Model Monitoring - Detect drift, skew, and anomalies in production
• Prediction endpoints with auto-scaling and load balancing
• Batch prediction for large-scale inference jobs
• A/B testing and traffic splitting between model versions
• Feature Store - Managed feature serving and reuse
• Vertex AI Matching Engine - High-performance vector similarity search

Use Cases and Applications

Vertex AI serves a wide range of AI and ML use cases across industries:

• Building conversational AI with Gemini and PaLM models
• Document understanding and intelligent document processing
• Custom recommendation systems using AutoML or custom models
• Computer vision applications (object detection, image classification)
• Predictive analytics on structured business data
• Natural language processing (sentiment analysis, entity extraction)
• Content generation (text, images, code) with foundation models
• Fraud detection and anomaly detection at scale
• Supply chain optimization and demand forecasting
• Healthcare AI for medical imaging and clinical decision support

Vertex AI vs Other AI Platforms

Compared to AWS SageMaker and Azure Machine Learning, Vertex AI differentiates itself through tight integration with Google Cloud services and access to Google's proprietary AI models. The platform provides seamless integration with BigQuery for data preparation, Cloud Storage for model artifacts, and Cloud Run for serverless deployment. Vertex AI's AutoML capabilities are particularly strong for no-code users, while custom training supports advanced use cases. The platform's access to Google's foundation models (Gemini, PaLM, Imagen) provides capabilities not available on competing platforms without third-party integrations.

However, Vertex AI requires commitment to the Google Cloud ecosystem and may have a steeper learning curve for teams unfamiliar with GCP. Pricing can be complex with separate charges for training, prediction, and storage. For organizations already using Google Cloud or requiring access to Google's AI models, Vertex AI provides a comprehensive, production-ready ML platform. The combination of AutoML for rapid prototyping and custom training for advanced needs makes it suitable for teams of varying skill levels.

Getting Started with Vertex AI

To start using Vertex AI, you'll need a Google Cloud account and project. The platform is accessible through the Google Cloud Console, gcloud CLI, Python SDK, or REST API. For beginners, AutoML provides the fastest path to a working model—simply upload your dataset (images, text, or structured data), select your prediction target, and let Vertex AI train and deploy a model automatically. For developers, Vertex AI Workbench provides managed Jupyter notebooks with pre-installed ML frameworks and seamless access to Google Cloud resources.

Google provides extensive documentation, quickstart guides, and sample notebooks covering common use cases. The Vertex AI Model Garden offers one-click deployment of pre-trained models, making it easy to experiment with foundation models before building custom solutions. For production deployments, Vertex AI Pipelines enable automated, reproducible ML workflows that can be triggered on schedules or events. The platform includes built-in security features (VPC Service Controls, Customer-Managed Encryption Keys) for enterprise compliance requirements.

Integration with 21medien Services

21medien leverages Google Vertex AI as part of our multi-cloud AI strategy, particularly for clients requiring Google Cloud infrastructure or Google's proprietary AI models. We use Vertex AI for deploying Gemini-based conversational AI applications, building custom computer vision models with AutoML, and creating production ML pipelines for enterprise clients. Our team provides Vertex AI consulting and implementation services, helping organizations design ML architectures, optimize training workflows, and deploy production-ready AI systems. We specialize in hybrid deployments that combine Vertex AI with other cloud providers or on-premises infrastructure.

Pricing and Access

Vertex AI uses pay-as-you-go pricing with separate charges for training, prediction, and storage. AutoML training costs vary by data type: ~$3.50/hour for tables, ~$20/hour for vision, ~$35/hour for NLP. Custom training pricing depends on machine type: CPU-only instances start at $0.10/hour, GPUs add $0.60-2.50/hour (T4-A100), TPUs cost $1.35-8/hour. Prediction pricing is based on instance type and uptime, with online prediction starting at $0.06/hour for CPU-only endpoints. Foundation model access (Gemini, PaLM) uses token-based pricing: ~$0.0001-0.0005/1K input tokens, ~$0.0003-0.0015/1K output tokens. Storage costs $0.05/GB-month. Google Cloud offers free tier credits ($300 for new users) and committed use discounts for sustained workloads. Enterprise pricing with custom SLAs is available through Google Cloud sales.