Case Study: Machine learning drives down operational costs in media & entertainment industry

From image denoising to audio processing and object recognition, each of this entertainment technology company’s brands relies to some extent on machine learning. But as data science needs evolved, the organisation’s homegrown machine learning platform struggled to keep up with new use cases.

To satisfy the shifting data science requirements of its brands in a cost-effective way, the company decided to replace its legacy experiment manager software with Charmed Kubeflow, delivered and fully managed by Canonical. The transformation is set to unlock tremendous savings in both costs and manpower, and the modernised platform will support 800 machine learning experiments per week.

“Partnering with Canonical lets us concentrate on our core business. Our data scientists can focus on data manipulation and model training rather than managing infrastructure.”

Machine Learning Engineer

Read the case study to discover how:

• Charmed Kubeflow is providing the foundation for a shared, cross-brand machine learning platform
• The customer is saving time and resources by offloading Kubeflow operations to Canonical through the fully managed service
• The platform supports 800 machine learning experiments per week

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