SCALABLE ML DATA PROCESSING ON AWS
OVERVIEW
Our enterprise client needed a scalable AWS-based platform for processing, storing, and visualizing large-scale machine learning (ML) files related to carbon footprint and sustainability data, delivered by third-party providers. The platform needed to efficiently handle large data volumes while providing responsive, interactive data visualization for end users.
THE CHALLENGE
Processing massive machine learning (ML) files often exhausts RAM and introduces I/O bottlenecks, long processing times, and challenges in data transfer and storage. These workloads require a high-performance architecture that is complex to design, time-consuming to build, and expensive to operate and maintain. Large datasets also demand significant storage capacity and efficient resource management.
Additionally, visualizing complex ML data requires a responsive user experience despite large data volumes. Because the ML files are provided by third-party vendors, the platform also needed to support automated ingestion through externally initiated file uploads.
OUR SOLUTION
We designed a cloud-native, containerized architecture to efficiently process large ML files. Direct file uploads from third-party providers trigger automated, event-driven workflows that ingest and process data without manual intervention. Shared cloud storage manages large datasets without relying on local server capacity, while container orchestration enables consistent deployments and seamless scaling. An automated CI/CD pipeline streamlines software delivery, and a decoupled architecture separates ingestion, processing, storage, and visualization to improve performance, maintainability, and flexibility. A lightweight web interface provides fast, interactive exploration of processed ML data.
KEY FEATURES
• Scalable File Processing: Efficiently processes large ML files without exhausting RAM by leveraging distributed cloud resources.
• Decoupled Architecture: Separates ingestion, processing, storage, and visualization to improve scalability and maintainability.
• Automated Deployment Pipeline: Uses CI/CD to enable fast, reliable, and repeatable deployments.
• Third-Party Integration: Supports seamless file uploads from external providers, triggering automated workflows.
• High-Performance Storage: Utilizes shared file systems to efficiently manage large data volumes with low latency.
• Containerized Environment: Ensures consistency across environments while simplifying scaling and maintenance.
• Interactive Data Visualization: Provides a responsive interface for exploring complex ML datasets.
GLOBAL IMPACT/RESULTS
• Reduced infrastructure complexity and ongoing maintenance effort.
• Improved processing performance for large machine learning datasets while minimizing memory and I/O bottlenecks.
• Lowered infrastructure costs through managed AWS services and on-demand resource scaling.
• Accelerated software releases with an automated CI/CD pipeline.
• Improved data availability through automated third-party file ingestion.
• Delivered a faster, more responsive experience for exploring carbon footprint and sustainability datasets.
TECHNOLOGIES & CAPABILITIES
Docker / Amazon Elastic Container Registry (ECR)
AWS Elastic Beanstalk
Amazon Elastic File System (EFS)
AWS CodePipeline
AWS CDK (Infrastructure as Code)
Streamlit
CONCLUSION
The platform provides a scalable, maintainable foundation for processing and visualizing large-scale machine learning datasets. By leveraging cloud-native technologies and automation, it enables efficient operations today while supporting future growth and increasing data volumes.
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