To get started using kubernetes, we defer to the kubernetes documentation.

Kubernetes can be useful in your ML workflows if you need access to other infrastructure resources. For example:

  • Deploying and serving models with a service mesh
  • Deploying and serving arbitrary applications, such as Jupyter Notebooks or documentation sites.
  • Accessing resources that are visible from your internal k8s cluster, such as:
    • Databases, i.e. Presto, Hive
    • Storage, i.e. HDFS
    • Distributed data processing, such as Dask or Spark
    • Specialized computing such as GPUs.
    • Integration with experiment tracking systems.
  • Access to secrets.
  • Native resource management and kubernetes for scalability and resiliance.
  • Interoperability with machine learning or data pipelines, such as Argo, MLFlow, Prefect, etc.
  • … and much more.

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