AWS Architecture
For AI & Enterprise
AWS offers the most comprehensive cloud platform with the deepest AI/ML services, widest global infrastructure, and most mature enterprise features. We design AWS architectures optimized for AI training, real-time data processing, and IoT/robotics workloads.
AWS Services for AI Workloads
Amazon SageMaker
The most complete ML platform for building, training, and deploying models at scale. We configure SageMaker Studio for your data science teams, set up distributed training on GPU clusters, implement MLOps pipelines with Model Registry, and deploy endpoints with auto-scaling. Full lifecycle ML management.
Amazon Bedrock
Access foundation models (Claude, Llama, Titan) through a unified API. Fine-tune on your data, deploy privately, and maintain enterprise security. No infrastructure management required.
Amazon Q
Enterprise AI assistant that connects to your business data. Build custom AI applications for employees without exposing data to public models.
Amazon Textract
Extract text, tables, and forms from documents automatically. Perfect for invoice processing, contract analysis, and digitization projects.
Amazon Transcribe
Speech-to-text with custom vocabulary support. Vietnamese, English, Korean, and 100+ languages. Call center analytics and meeting transcription.
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Real-Time Data with Kinesis
Process millions of events per second for telemetry, analytics, and AI inference.
Kinesis Data Streams
Ingest and process streaming data in real-time. Configure shards for throughput, set retention periods, and enable enhanced fan-out for multiple consumers.
Kinesis Data Analytics
Run SQL or Apache Flink on streaming data. Real-time aggregations, anomaly detection, and pattern matching without managing infrastructure.
Kinesis Data Firehose
Load streaming data to S3, Redshift, Elasticsearch, or Splunk. Automatic batching, compression, and encryption. Zero administration.
AWS IoT for Robotics Integration
99.99% uptime, guaranteed
Your systems stay online even when entire data centers go down. Built-in redundancy, no extra effort.
AWS IoT Greengrass
Deploy cloud capabilities to edge devices and robots. Run Lambda functions locally, sync with the cloud when connected, and execute ML inference on-device. Perfect for autonomous mobile robots (AMRs), industrial arms, and humanoid systems that need local processing with cloud orchestration.
Robot fleet management • Real-time path planning • Vision processing at edge • Predictive maintenance • Multi-robot coordination
AWS IoT Core
Connect billions of devices securely. MQTT, HTTP, and WebSocket protocols. Device shadows for state management, rules engine for routing.
AWS RoboMaker
Simulation environment for robotics applications. Test robot software at scale before deploying to physical hardware. ROS integration included.
AWS Panorama
Computer vision at the edge. Deploy vision models to cameras and edge appliances. Quality inspection, safety monitoring, inventory tracking.
AWS IoT SiteWise
Collect and analyze industrial equipment data. OPC-UA integration, asset modeling, and dashboards for manufacturing operations.
GPU Instances for AI Training
| INSTANCE TYPE | GPUs | GPU MEMORY | BEST FOR | SPOT SAVINGS |
|---|---|---|---|---|
| p5.48xlarge | 8x H100 | 640 GB HBM3 | Large model training (70B+) | Up to 90% |
| p4d.24xlarge | 8x A100 | 320 GB HBM2e | Model training & fine-tuning | Up to 70% |
| g5.xlarge | 1x A10G | 24 GB GDDR6 | Inference & small training | Up to 70% |
| inf2.xlarge | 1x Inferentia2 | 32 GB | Cost-optimized inference | Up to 50% |
| trn1.32xlarge | 16x Trainium | 512 GB HBM | Cost-optimized training | Up to 50% |
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