Google Cloud
Data & AI Leadership
Google Cloud leads in data analytics and AI research. Vertex AI provides cutting-edge ML capabilities, BigQuery delivers unmatched analytics performance, and TPUs offer the most cost-effective AI training compute. Built by the company that invented modern AI.
Vertex AI Platform
Google's unified ML platform-from research to production.
Vertex AI
The most advanced unified ML platform combining AutoML for no-code model building with custom training for data scientists. Access Gemini models through the API, fine-tune on your data, and deploy with one-click endpoints. Includes Model Garden for 130+ foundation models, Feature Store for ML features, and Matching Engine for vector search across large datasets.
Gemini API
Access Google's most capable AI model. Multimodal understanding (text, images, video, code), long context windows, and enterprise-grade reliability.
Matching Engine
Billion-scale vector search with sub-millisecond latency. Perfect for RAG systems, recommendations, and semantic search applications.
AutoML
Train custom models without ML expertise. Tables, Vision, Natural Language, and Video. Google's ML research automated for your use cases.
Feature Store
Centralized repository for ML features. Share features across teams, ensure consistency between training and serving, and track feature lineage.
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Engage Ghost →BigQuery: Analytics at Google Scale
Query petabytes in seconds. No infrastructure management. Pay only for what you use.
BigQuery ML
Train ML models directly in BigQuery using SQL. Linear regression, clustering, time-series forecasting, and deep learning without moving data.
Streaming Inserts
Ingest streaming data in real-time. Query data immediately after arrival. Perfect for live dashboards and real-time analytics.
Data Sharing
Analytics Hub for secure data sharing across organizations. Share datasets, ML models, and insights without copying data.
Real-Time Data with Dataflow
Cloud Pub/Sub
Global messaging with guaranteed delivery. Ingest from any source at any scale. At-least-once and exactly-once semantics available.
Dataflow
Apache Beam-based stream and batch processing. Unified programming model, auto-scaling, and exactly-once processing guarantees.
Dataproc
Managed Spark and Hadoop for batch processing. Auto-scaling clusters, spot VMs for cost savings, and direct BigQuery integration.
GCP for Connected Devices
Edge TPU & Coral
Run TensorFlow Lite models on edge devices with dedicated AI accelerators. The Coral Edge TPU delivers 4 TOPS at ultra-low power-perfect for robots, cameras, and industrial equipment. Deploy computer vision, NLP, and sensor fusion directly on devices without cloud round-trips.
On-device object detection • Real-time pose estimation • Sensor fusion • Anomaly detection • Natural language commands
Cloud IoT Core
Device management for large fleets. MQTT and HTTP protocols, device registry, and configuration management. Connect millions of devices securely.
Edge Manager
Deploy and manage ML models on edge devices. OTA updates, model versioning, and A/B testing for edge AI applications.
Video Intelligence
Pre-trained models for video analysis. Object tracking, scene detection, and action recognition. Process video streams from cameras and robots.
Speech & Vision APIs
World-class speech recognition and computer vision. Deploy Google's AI capabilities to your robots and devices via simple APIs.
TPUs: AI-Optimized Hardware
Google-designed chips that make large-scale AI training cost-effective.
| COMPUTE TYPE | PERFORMANCE | BEST FOR | COST VS GPU |
|---|---|---|---|
| TPU v5p | 459 TFLOPS per chip | Large model training (100B+) | 2-3x cheaper |
| TPU v5e | 197 TFLOPS per chip | Inference & fine-tuning | 3-4x cheaper |
| A3 (H100) | 8x H100 per VM | Maximum flexibility | Baseline |
| A2 (A100) | 16x A100 per VM | General ML training | Baseline |
| Edge TPU | 4 TOPS | On-device inference | $0 (no cloud) |
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