Optimizing an End-to-End Analytics Platform

Solution End-to-End Data Platform Enhancement
Solution End-to-End Data Platform Enhancement
Industry Cross-Industry
Region North America
Context
By seamlessly integrating data engineering and MLOps practices, MathCo’s end-to-end data analysis platform reduced overall runtime by 340 DBU, leading to annual savings exceeding $400k. This cost-effective solution enhanced the client’s data engineering maturity within the AWS ecosystem, removing redundancy through automation and leveraging model monitoring to effectively manage the platform.  
Problem statement

Our client was looking for a cost-effective solution for boosting their data engineering maturity in the AWS ecosystem. Their Operations team wanted to develop a modular, scalable, and secure framework in Airflow with Databricks that would embed the best engineering and MLOps practices to productionize and optimize data workflows while reducing costs and overall runtime. Additionally, they intended to automate the repetitive tasks and utilize model monitoring to manage the platform. 

Impact

  • Reduced runtime of Merchandizing MLOps initiative by 65% 
  • Decreased cost of Operations department by 80% 
  • Reduced Personalization runtime by 48% 
  • Saved Databricks unit (DBU) hours by nearly 340 hours per month and achieved annual savings of over $400k for the company 

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