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MLOps Automation Platform for Retail and Décor Software Solutions
  1. case
  2. MLOps Automation Platform for Retail and Décor Software Solutions

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MLOps Automation Platform for Retail and Décor Software Solutions

exposit.com
Retail
Consumer products & services

Operational Inefficiencies in ML and Data Management

Manual data management processes requiring custom scripts caused delays in ML model training. Inefficient ML workflows with time-consuming kernel recompilation and infrastructure management slowed product updates. Poor monitoring systems led to service disruptions and suboptimal MTBF/MTTR metrics.

About the Client

Software solutions provider for retail and finishing materials/ décor brands, specializing in AI-driven tools for interior design and product visualization

Key Objectives for MLOps Implementation

  • Automate end-to-end data management workflows
  • Optimize ML model training and deployment processes
  • Implement real-time performance monitoring systems
  • Improve system reliability and reduce operational downtime

Core System Functionalities

  • Airflow pipeline automation for data preprocessing
  • Amazon SageMaker integration for model training/deployment
  • CloudWatch and Prometheus metrics tracking with NVIDIA Triton
  • Serverless computing architecture for scalable processing

Technology Stack Requirements

Amazon SageMaker
Apache Airflow
AWS Lambda
AWS Glue ETL
Amazon CloudWatch
Prometheus
NVIDIA Triton

System Integration Needs

  • AWS EC2 instance management
  • Vast.ai infrastructure (legacy)
  • Amazon S3 data storage
  • ERP systems (Oracle PeopleSoft)

Operational Requirements

  • Horizontal scalability for peak workloads
  • 99.9% system availability SLA
  • Role-based access control (RBAC)
  • Sub-second API response times for monitoring dashboards

Expected Business Impact of MLOps Implementation

Data specialists to reduce operational hours by 60% through automation. ML engineers to cut model update times by eliminating manual infrastructure tasks. System reliability improvements to reduce downtime by 75% and accelerate time-to-market for AI features. Enhanced cross-team collaboration between business and technical units.

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