MLOps Acceleration for Cycling Performance Analysis

AIB2BSMBAutomation
Written By Bruno Carrier

Overview

We introduced an analytics framework that harmonizes data ingestion, modeling, and delivery for coaches. The solution accelerates iteration while sustaining consistent evaluation standards. Routine pipelines are automated so staff focus on strategy and analysis.

Challenges

The professional cycling team encountered challenges in fully leveraging advanced analytics, including difficulties scaling data preparation and limited resources for identifying and training the right models. With the goal of turning data science into a true competitive advantage, the team sought to empower its data group to deliver meaningful impact.

Results and Benefits

Accelerated model delivery cycles. Improved prediction accuracy for training plans. Unified analytics to inform coaching decisions.

AWS Services

Amazon SageMaker, AWS Step Functions, AWS Glue, Amazon S3, AWS Lambda, Amazon Athena, Amazon QuickSight.

Customer details

  • Industry: Sport

  • Company Type: Mid-sized Enterprise

  • Location: France

  • Project Timeline: 2 weeks

Bruno Carrier https://benilar.com
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