2 Matching Annotations
- Last 7 days
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Data scientists, MLOps engineers, or AI developers, can mount large language model weights or machine learning model weights in a pod alongside a model-server, so that they can efficiently serve them without including them in the model-server container image. They can package these in an OCI object to take advantage of OCI distribution and ensure efficient model deployment. This allows them to separate the model specifications/content from the executables that process them.
The introduction of the Image Volume Source feature in Kubernetes 1.31 allows MLOps practitioners to mount OCI-compatible artifacts, such as large language model weights or machine learning models, directly into pods without embedding them in container images. This streamlines model deployment, enhances efficiency, and leverages OCI distribution mechanisms for effective model management.
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- Jan 2023
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We’re also not going deep here on MLops or LLMops tooling, which is not yet highly standardized and will be addressed in a future post.
first mention of LLMops I've seen in the wild
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