Managing Machine Learning in Production with Kubeflow and DevOps
Kubeflow has helped bring machine learning to Kubernetes, but theres still a significant gap relative to how to productize these workloads. While DevOps and GitOps have made huge traction in recent y …
| Talk Title | Managing Machine Learning in Production with Kubeflow and DevOps |
| Speakers | David Aronchick (Head of OSS Machine Learning, Microsoft) |
| Conference | KubeCon + CloudNativeCon Europe |
| Conf Tag | |
| Location | Barcelona, Spain |
| Date | May 19-23, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Kubeflow has helped bring machine learning to Kubernetes, but there’s still a significant gap relative to how to productize these workloads. While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on ways to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will demonstrate how to run an E2E machine learning system using nothing more than Git. This will integrate DevOps, data and ML pipelines together, and show how to use multiple workload orchestrators together. While the examples will be run using Azure Pipelines and Kubeflow, we will also show how to extend these platforms to any orchestration tool.
