Ludex

Transforming Sports Card Collections with
Scalable Data Solutions

Optimizing Ludex’s Data Infrastructure with End-to-End MLOps and AI-Powered Automation

2020 - San Francisco, CA

Software Development

51-200 employees

Overview

Ludex is a sports and trading card scanning app that helps collectors accurately identify and track the value of their collections. Ludex provides a seamless and intelligent way for sports and trading card collectors to digitize and assess their collections. By using AI and machine learning, the platform automates card identification, valuation, and organization, making it accessible for both experienced collectors and newcomers.
The platform uses AI and machine learning to automate card identification, valuation, and organization, making it accessible to seasoned collectors and newcomers. Folio3 Data Services collaborated with Ludex to develop a scalable data infrastructure, automate data ingestion pipelines, and streamline the integration of machine learning models using end-to-end MLOps solutions.

Architecture Diagram

Below is a simplified architecture diagram illustrating how Folio3 Data Services implemented the scalable solution for Ludex:

Explanation

Mobile App

Users scan and upload images of trading cards using the Ludex app.

AWS API Gateway

Facilitates communication between the app and backend services.

AWS Lambda

Handles data validation and reprocessing and triggers additional processes.

Amazon S3

Stores raw and intermediate data temporarily before further processing.

AWS Glue

Manages ETL pipelines to clean, transform, and structure data.

Snowflake

Serves as the primary data warehouse, storing processed data for querying and analysis.

Amazon SageMaker

Deploys machine learning models to analyze and predict card values.

AWS Lambda (Inference)

Runs model predictions in real-time and sends results back to the app.

Looker/Tableau

Provides reporting and dashboards for internal monitoring and customer insights.

The Challenge

Optimizing Data Management for Scalability, Automation, and Real-Time Valuations

Ludex encountered several challenges in managing the vast amounts of data generated through sports card scans, valuations, and user interactions. Folio3 Data Services addressed these challenges by implementing an end-to-end MLOps solution, optimizing Ludex’s data infrastructure for automation, scalability, and accuracy.

High-Volume Data Processing: With thousands of new sports cards scanned daily, Ludex needed a scalable data infrastructure to ingest, store, and process card attributes efficiently.

Automated Data Transformation for Accuracy: The manual process of identifying and categorizing cards was time-consuming and prone to errors. Ludex required an AI-driven solution to automate data transformation and ensure accurate valuations.

Real-Time Card Valuations: To enhance the user experience, Ludex needed a system capable of providing instant, data-driven insights on card values, trends, and market fluctuations.

Scalability for an Expanding Database: As Ludex continuously added new cards and users, the data solution had to scale seamlessly without impacting performance.

Seamless Integration with AI and Cloud Infrastructure: Ludex relied on machine learning models and cloud-based solutions, requiring a streamlined data pipeline for model training, inference, and deployment.

The Solution: Cloud-Native Data Engineering for Scalable Reporting and Insights

Folio3 Data Services deployed a comprehensive data infrastructure that automated card identification and valuation. By implementing advanced data ingestion, transformation, and processing pipelines, we significantly reduced manual intervention and accelerated the speed of data integration.

Key components of the solution included:

MLOps Deployment & Automation

Automated model training and deployment workflows with CI/CD pipelines and AWS Lambda, ensuring continuous optimization and performance monitoring.

Cloud-Native Architecture

Hosted the solution on AWS for seamless scalability and high availability, reducing infrastructure overhead.

Real-time Data Integration

Integrated Amazon SageMaker to facilitate batch and real-time inference, enhancing the accuracy and efficiency of card recognition.

Data Ingestion & ETL Pipelines

Designed scalable ETL pipelines using AWS Glue and Snowflake to automate card data extraction, transformation, and loading.

AI-Driven Data Processing

Leveraged ML models to extract and analyze card attributes, including player name, team, year, and value trends.

Technologies Used

Cloud & Data Storage

ETL & Data Pipelines

Machine Learning & AI

Data Transformation

CI/CD & MLOps

Analytics & Reporting

Results & Achievements

With Folio3’s data infrastructure and MLOps expertise, Ludex achieved the following:

80% Reduction in Manual Effort

Automated data ingestion saved over 5+ hours per day of manual work.

50% Faster Data Processing

Optimized batch processing improved data recognition and valuation speeds.

Seamless Scalability

Cloud-native architecture enabled Ludex to scale data ingestion by 3x, accommodating new card releases effortlessly.

Enhanced User Experience

Real-time analytics improved card recognition accuracy, ensuring precise valuations for collectors.

A Data-Driven Transformation for Scalable Sports Card Valuation

Folio3 Data Services provided Ludex with a robust data and AI-driven framework, transforming their card valuation system into a scalable, automated, and data-centric platform. By using advanced data pipelines, machine learning automation, and cloud infrastructure, Ludex offers its users an accurate, real-time, and hassle-free card collection experience.