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Maria Vechtomova

As seen in: Substack
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Articles by Maria Vechtomova

Lessons learned from migrating models to Unity Catalog

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By Maria Vechtomova, Import Mlflow
| Substack
Since Databricks introduced Unity Catalog, all features require Unity Catalog-enabled workspaces to work properly. You should now also register models in Unity Catalog since the Workspace model registry is marked as “legacy”. MLflow experiments are still at the workspace level. In this article, we will discuss changes introduced due to the Unity Catalog and how they impacted us. But first, let’s explain our requirements. Marvelous MLOps Substack is a reader-supported publication.
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Model serving architectures on Databricks

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By Maria Vechtomova
| Substack
Many different components are required to bring machine learning models to production. I believe that machine learning teams should aim to simplify the architecture and minimize the amount of tools they use. Databricks provides a lot of features and allows to do MLOPs end-to-end, including model serving. Regarding model serving, Databricks has two interesting features: Model Serving and Feature serving. Note that Unity Catalog is required for serving.
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Getting started with Databricks Feature Serving

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By Maria Vechtomova, Served EntityInput
| Substack
Databricks has recently announced the general availability of the Feature Serving in March 2024. This functionality is useful when exposing the data available on Databricks to other applications. To make it a bit more clear when you need Feature Serving vs Model Serving, see a diagram below. Marvelous MLOps Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
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