Graaho Technologies transformed its personalization engine, ALGOREC, using AWS’s scalable infrastructure and machine learning services. This enabled the delivery of real-time, context-aware recommendations to users—boosting engagement, increasing sales, and reducing operational costs through automation and intelligent infrastructure. 

To meet the rising demand for hyper-personalized digital interactions, Graaho needed to: 

• Build a robust recommendation engine based on machine learning and behavioral data. 

• Enable seamless data processing for real-time predictions and scheduled model training. 

• Ensure cost-effective, scalable infrastructure capable of handling fluctuating loads. 

• Maintain strict security and data compliance throughout the ML lifecycle. 

• Graaho Technologies built a modular, full-stack ML architecture with real-time inference and analytics using the following AWS services: User Authentication & API Management: 
  • Amazon Cognito for secure user sign-in and access control. 
  • Amazon API Gateway to expose and manage backend APIs. 

  Compute & ML Orchestration: 

  • Amazon EC2 for hosting backend logic, inference services, and orchestration tools like Apache Airflow. 

  Storage & Data Processing: 

  • Amazon S3 for storing training datasets, artifacts, and behavioral data. 
  • Amazon RDS (PostgreSQL) for transactional data and feature generation. 

  ML Training & Inference Pipelines: 

  • Apache Airflow orchestrates training pipelines. 
  • EC2-based inference servers deliver real-time recommendations based on user activity. 

  Messaging & Communication: 

  • Amazon SQS for decoupled messaging between services. 

  Monitoring & Observability: 

  • Amazon CloudWatch for performance monitoring, logging, and alerting. 

• • Reduced TCO: Eliminated hardware CAPEX and minimized OPEX with elastic resources. 
  • Improved Agility: Automated pipelines and patching saved engineering time. 
  • Governance Simplified: IAM, encryption, and compliance tools built into AWS architecture. 
  • Support: AWS Support Services accelerated architecture validation and performance tuning. 

   

• Modular, Cloud-Native architecture is key to agile scaling and faster iteration. 

• Early integration of orchestration tools like Apache Airflow significantly reduces ML ops complexity. 

• Real-time personalization success relies heavily on robust data pipelines and observability. 

• AWS support and architectural best practices were crucial to rapid deployment and optimization. 

“AWS enabled us to bring ALGOREC to life—faster, smarter, and more securely than we could have imagined. The support from AWS architects, combined with the flexibility of AWS services, empowered our team to focus on innovation while confidently scaling to meet real-time personalization demands.” 

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