9 Matching Annotations
- Mar 2022
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Neptune.io PoV.
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a Data Scientist or a Researcher,
Analyze pros & cons based on the the well-written distinction between these roles: - Data scientist/Researcher - ML Engineer - Project Lead
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neptune.ai neptune.ai
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Somewhat biased for neptune, omitting things unsupported on its side,
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www.netguru.com www.netguru.com
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Comparing:
Experiment-tracking alone tools:
- neptune.ai
- Wandb
Full-lifecycle tools:
- MLflow/Databricks ...
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- Jan 2022
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www.oreilly.com www.oreilly.com
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Explain the need for [[mlflow]] and more tools for data/model governance.
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- May 2021
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medium.com medium.com
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In short, MLflow makes it far easier to promote models to API endpoints on various cloud vendors compared to Kubeflow, which can do this but only with more development effort.
MLflow seems to be much easier
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Bon Appétit?
Quick comparison of MLflow and Kubeflow (check below the annotation)
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MLflow is a single python package that covers some key steps in model management. Kubeflow is a combination of open-source libraries that depends on a Kubernetes cluster to provide a computing environment for ML model development and production tools.
Brief comparison of MLflow and Kubeflow
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- Feb 2020