Infrastructure

AI Infrastructure Built for Advertising Scale

Processes 2B+ signals daily across distributed GPU clusters with sub-50ms inference โ€” purpose-designed for real-time advertising intelligence at global scale.

End-to-End AI Data Pipeline

From raw advertising signal to actionable intelligence in under 50ms.

๐Ÿ“ก
Signal Ingestion
2B+ events/day
โšก
Stream Processing
Amazon Kinesis
๐Ÿ—„๏ธ
Feature Store
Redis + S3
๐Ÿง 
ML Inference
GPU ยท p99 47ms
๐Ÿ“Š
Decision Engine
SageMaker
๐Ÿš€
API Response
REST / WS

Model Architecture

๐Ÿ”ฎ

Ensemble Fraud Models

Stacked ensemble of XGBoost, LightGBM, LSTM temporal models, and Isolation Forest โ€” voted by a neural meta-learner. Trained on 890M fraud-labeled samples.

XGBoostLightGBMLSTMIsolation Forest
๐Ÿ“ˆ

Conversion Prediction

Transformer-based architecture with multi-task learning across conversion types. Causal inference for attribution modeling, federated learning for privacy preservation.

TransformerMulti-TaskCausal ML
โš™๏ธ

Optimization Agents

Reinforcement learning agents using contextual bandits for bid optimization, deep Q-learning for budget allocation, and Thompson sampling for affiliate A/B testing.

Contextual BanditDeep Q-Learning

Machine Learning Stack

Our ML platform is built on battle-tested open-source frameworks, trained on proprietary advertising datasets collected over years of real-world operation.

Python 3.11 PyTorch 2.x TensorFlow 2.x Scikit-learn XGBoost Hugging Face Ray Tune MLflow

Real-Time Streaming Analytics

Every advertising event is processed as it happens โ€” enabling real-time model scoring and instant fraud decisions without batch latency.

Apache Kafka Apache Spark Redis Terraform Kubernetes Prometheus
Platform Performance Metrics
Events processed / sec24,847
Inference latency p5012ms
Inference latency p9947ms
GPU utilization87%
API uptime (30d SLA)99.97%
Model retrain cadenceEvery 6h
Training Dataset Scale
0B+
Labeled click events
0M+
Fraud-labeled samples
0B+
Conversion records
0+
Geo markets

Amazon Web Services (AWS)

Enterprise-grade cloud infrastructure for compute, storage, and real-time data pipelines โ€” deployed across multiple AWS regions for global resilience.

โš™๏ธ

Amazon EC2

GPU compute clusters for real-time inference and model serving

๐Ÿ—„๏ธ

Amazon S3

Scalable data lake for training datasets and model artifacts

โšก

Amazon Kinesis

Real-time streaming analytics processing 2B+ events daily

๐Ÿง 

Amazon SageMaker

Managed ML pipeline for model training, validation & deployment

๐Ÿ”€

Amazon DynamoDB

Sub-millisecond feature store for real-time model feature lookups

๐ŸŒ

AWS CloudFront

Global CDN for low-latency API delivery across 200+ countries

NVIDIA GPU Accelerated Training

Our model training infrastructure leverages NVIDIA A100 GPUs via AWS p4d instances, enabling full model retraining every 6 hours on 47B+ labeled advertising events.

NVIDIA A100 80GB CUDA 12.x TensorRT Inference NVIDIA RAPIDS AWS p4d.24xlarge NVIDIA Triton Server
0ร—
Faster training vs CPU
0h
Model retrain cadence
0B
Training samples
87%
Avg GPU utilization

Enterprise-Grade Security & Compliance

๐Ÿ”

SOC 2 Type II

Independently audited security controls and data handling.

๐ŸŒ

GDPR Compliant

EU data residency options with privacy-by-design architecture.

๐Ÿ”’

AES-256 + TLS 1.3

End-to-end encryption at rest and in transit. Zero-knowledge.

โšก

99.97% Uptime SLA

Multi-region redundancy with automatic failover. 24/7 SOC.

โœ“ Done!