Lsports provides an innovative sports betting data API for the sports betting industry. They are a leading provider of high-quality live sports data feeds.
The Challenge
Lsports had a large data repository in their data warehouse.
Lsports’ Data Science team wanted to be able to create machine learning models from this data. They were looking for a platform that could pre-process, train and deploy machine learning models in a quick and scalable manner.
The Solution
A full machine learning pipeline was designed and built on top of the AWS SageMaker platform. The solution delivered a pipeline that controls data preparation, model training, and model deployment. All stages were implemented on AWS SageMaker. This pipeline was wrapped in an AWS code pipeline to control triggers and manual approvals. We also included enablement sessions to ramp-up the team to SageMaker, enabling them to manage experiments, and data preprocessing inside the SageMaker platform on their own.
The Results
The customer has now gone through an enablement process that lets them step up their machine learning process and move to a higher level of automation with added abilities for rapid innovation through robust machine learning lifecycle management. Some of the automations include:
- Create reproducible workflow and models.
- Easy deployment of high-precision models in any location.
- Effective management of the entire machine learning lifecycle.
- Machine learning resource management system and control.
Daniel Netzer, Senior SA @Lsports:
“The Data team at CloudZone has been a pleasure to work with. They are quick and responsive, and always follow through on commitments. The development process for our machine learning pipeline was seamless and now we have a working ML platform with Sagemaker. It’s amazing how much we’ve progressed in just a few weeks!”
