Solution Brief

Kaptain: Powered by Kubeflow

There is a natural fit for artificial intelligence (AI) and machine learning (ML) workloads on Kubernetes, as it is well-suited to meet the scalability needs of AI/ML jobs as well as embracing the continuous development nature of AI/ML models.

There are, however, several significant challenges to be overcome: 
  • High risk: Up to 87% of ML initiatives are abandoned before they reach production.
  • Long time to value: For those initiatives that do make it to production, it can take more than three months for a single model to be deployed. Software provisioning at enterprises can take weeks or even months, which adds time and delays obtaining value.
  •  New technology: ML platforms for the big data and deep learning era have only been around since 2016, with few technologies that are cloud native.
  • Complexity: build-or-buy decisions for scalable platforms require immense knowledge of cloud-native infrastructure as well as the entire AIML landscape.

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