Building Enterprise-Grade ML Systems

Comprehensive showcase of production-ready machine learning engineering, featuring MLOps pipelines, real-time personalization, and scalable data infrastructure.

5 Production Systems
50K+ RPS Capacity
99.99% Availability
<100ms Latency
Explore Projects Live Demos
Real-time Analytics
Predictions/sec 1,247
Model Accuracy 97.3%
Latency 23ms
Feature Store
user_engagement_score Active
product_affinity Active
session_context Active
MLOps Pipeline
Data Validation
Model Training
Deployment

Featured Projects

Production-ready ML systems demonstrating enterprise-grade engineering practices

Real-Time Personalization Engine

High-performance recommendation system with sub-100ms latency, processing 50K+ requests per second with advanced ML models.

Python Flask Redis Kafka
Latency <100ms
Throughput 50K RPS
View Details

MLOps Pipeline & CI/CD

Comprehensive MLOps pipeline with automated training, validation, and deployment using GitHub Actions and Kubernetes.

GitHub Actions MLflow Kubernetes Docker
Deployment Time 3 min
Test Coverage 95%
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Scalable Feature Store

Enterprise-grade feature management with dual-storage architecture, lineage tracking, and real-time serving capabilities.

PostgreSQL Redis FastAPI SQLAlchemy
Features 1000+
Lookup Time 5ms
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Interactive ML Dashboard

Comprehensive monitoring and analytics dashboard with real-time model performance tracking and business intelligence.

Streamlit Plotly Prometheus Grafana
Dashboards 12
Metrics 50+
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Dynamic Rules Engine

Flexible business rules engine with real-time configuration updates, A/B testing capabilities, and performance optimization.

Go gRPC etcd Prometheus
Rules 500+
Evaluation 1ms
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System Architecture

Enterprise-grade architecture designed for scalability, reliability, and performance

System Architecture

Multi-Layer Architecture

Comprehensive system design with clear separation of concerns, featuring frontend interfaces, API gateways, core services, ML pipelines, and data infrastructure.

  • Microservices architecture
  • Event-driven communication
  • Horizontal scalability
  • Fault tolerance
MLOps Pipeline

Automated ML Lifecycle

End-to-end MLOps pipeline with automated training, validation, and deployment, featuring comprehensive CI/CD integration and monitoring.

  • Automated model training
  • Comprehensive validation
  • Blue-green deployment
  • Continuous monitoring
Data Flow

Real-time Data Processing

Comprehensive data flow architecture supporting both real-time streaming and batch processing with proper data lineage and quality monitoring.

  • Stream processing
  • Batch processing
  • Data lineage tracking
  • Quality monitoring
Deployment Architecture

Multi-Environment Deployment

Production-ready deployment architecture with development, staging, and production environments, featuring comprehensive CI/CD pipelines and monitoring.

  • Multi-environment setup
  • Infrastructure as code
  • Automated deployments
  • Comprehensive monitoring

Live Demos

Interactive demonstrations of production-ready ML systems

Real-Time Personalization

Experience the personalization engine in action with live event processing and recommendation generation.

Service Online
Event Dashboard API Docs

System Health Monitor

Real-time system health monitoring with service status, performance metrics, and infrastructure analytics.

Service Online
View Health Status

Feature Store API

Test the feature store API with interactive documentation and real-time feature retrieval.

Service Online
Feature Explorer API Docs

Let's Connect

Interested in discussing ML engineering opportunities or collaborating on innovative projects? I'd love to hear from you.

acepci@gmail.com LinkedIn Profile GitHub Repository