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Rewording in various docs #5192

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Jan 22, 2024
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Rewording in various docs #5192

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2 changes: 1 addition & 1 deletion doc/source/analytics/explainers.md
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![cat](cat.png)
![explanation](cat_explanation.png)

Seldon provides model explanations using its [Alibi](https://github.com/SeldonIO/alibi) Open Source library.
Seldon provides model explanations using its [Alibi](https://github.com/SeldonIO/alibi) library.

We support explainers saved using python 3.7 in v1 explainer server. However, for v2 protocol (using MLServer) this is not a requirement anymore.

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9 changes: 1 addition & 8 deletions doc/source/reference/release-0.4.1.md
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Expand Up @@ -4,7 +4,7 @@ A summary of the main contributions to the [Seldon Core release 0.4.1](https://g

## Black Box Model Explanations

By utlizing Seldon'sopen source Model Explanation library [Alibi](https://github.com/SeldonIO/alibi) we provide the ability to launch a model and an associated explainer for that model. At present we support the [Anchors](https://homes.cs.washington.edu/~marcotcr/aaai18.pdf) explanation technique for tabular text and image examples.
By utlizing Seldon's open source Model Explanation library [Alibi](https://github.com/SeldonIO/alibi) we provide the ability to launch a model and an associated explainer for that model. At present we support the [Anchors](https://homes.cs.washington.edu/~marcotcr/aaai18.pdf) explanation technique for tabular text and image examples.

An example SeldonDeployment for an image model with associated explainer is shown below:

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* We are available on the [RedHat Container Catalog](https://access.redhat.com/containers/?tab=overview#/registry.connect.redhat.com/seldonio/seldon-operator-0-4-0). Update to 0.4.1 soon.

[Join our slack community to discuss](https://join.slack.com/t/seldondev/shared_invite/enQtMzA2Mzk1Mzg0NjczLTJlNjQ1NTE5Y2MzMWIwMGUzYjNmZGFjZjUxODU5Y2EyMDY0M2U3ZmRiYTBkOTRjMzZhZjA4NjJkNDkxZTA2YmU).







2 changes: 1 addition & 1 deletion doc/source/tutorials/openshift_s2i.md
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Seldon aims to help organisations put their data science projects into production so they can decrease the time to get return on investment. By helping data scientists take their data science models and place them into production, scale them, get analytics and modify them Seldon allows data scientists to bridge the gap from development to production and use current dev-ops best practices in machine learning. Our core products run on top of Kubernetes and can be deployed on-cloud on on-premise. Integrating with enterprise ready Kubernetes distributions such as Openshift allows us to provide a solid foundation in which to supply our products for use in demanding verticals such as the FinTech sector.

[Seldon-Core](https://github.com/SeldonIO/seldon-core) is an open source project that provides scalable machine learning deployment running on [Kubernetes](https://kubernetes.io/). One of Seldon-Core’s goals is to allow data scientists to continue to construct their training and inference components using any of the many available machine learning toolkits, be that python based (e.g., TensorFlow, sklearn), R or Java (e.g., Spark, H2O) amongst many popular options. Seldon-Core will then allow them easily to package and run their runtime prediction modules on Kubernetes. To achieve this goal we need to make it easy for data scientists to take their source code and package it as a Docker-formatted container in the correct form such that it can be managed as part of a runtime microservice graph on Kubernetes by Seldon-Core. For this we utilize Openshift’s Source-to-Image open source library to allow any code to be packaged in the correct format with minimal requirements from the data scientist.
[Seldon-Core](https://github.com/SeldonIO/seldon-core) is a project that provides scalable machine learning deployment running on [Kubernetes](https://kubernetes.io/). One of Seldon-Core’s goals is to allow data scientists to continue to construct their training and inference components using any of the many available machine learning toolkits, be that python based (e.g., TensorFlow, sklearn), R or Java (e.g., Spark, H2O) amongst many popular options. Seldon-Core will then allow them easily to package and run their runtime prediction modules on Kubernetes. To achieve this goal we need to make it easy for data scientists to take their source code and package it as a Docker-formatted container in the correct form such that it can be managed as part of a runtime microservice graph on Kubernetes by Seldon-Core. For this we utilize Openshift’s Source-to-Image open source library to allow any code to be packaged in the correct format with minimal requirements from the data scientist.

## Seldon-Core Overview

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