16 lines
1.2 KiB
Markdown
16 lines
1.2 KiB
Markdown
---
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title: "MLOps"
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ring: assess
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quadrant: methods-and-patterns
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tags: [devops]
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---
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After spending some time diving into the world of data science and machine learning we're realizing our existing DevOps best practices aren't a perfect fit for the specific workflows we're seeing here.
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Data science is not only about code but also all about managing large datasets and models.
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Data is being analyzed, models are being trained in many iterations and then software needs to be deployed that does the actual prediction/inference.
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And this circle (see: CRISP-DM) will repeat over and over again during the development phase and after the first production release.
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"**MLOps**" extends the DevOps best practices in order to cover these new scenarios specific to machine learning workflows.
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[DVC](https://dvc.org/) helps dealing with large data sets and models by connecting external storage to your Git repositories and [CML](https://cml.dev/) helps integrating the CI/CD into your GitHub or GitLab workflows.
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Since we're already using Kubernetes extensively we're exploring [Kubeflow](https://www.kubeflow.org/) for running the full machine learning workflow on Kubernetes clusters.
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