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4
docs/clearml_serving/clearml_serving.md
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@ -43,8 +43,8 @@ solution.
* **CLI** - Secure configuration interface for on-line model upgrade/deployment on running Serving Services
* **Serving Service Task** - Control plane object storing configuration on all the endpoints. Support multiple separated
instance, deployed on multiple clusters.
* **Serving Service Task** - Control plane object storing configuration on all the endpoints. Supports multiple separate
instances, deployed on multiple clusters.
* **Inference Services** - Inference containers, performing model serving pre/post-processing. Also supports CPU model
inferencing.

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docs/clearml_serving/clearml_serving_tutorial.md
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@ -199,7 +199,7 @@ The default docker-compose installation is preconfigured with Prometheus and Gra
of both containers is *not* persistent. To add persistence, we recommend adding a volume mount.
You can also add many custom metrics on the input/predictions of your models. Once a model endpoint is registered,
adding custom metric can be done using the CLI.
adding custom metrics can be done using the CLI.
For example, assume the mock scikit-learn model is deployed on endpoint `test_model_sklearn`, you can log the requests
inputs and outputs (see examples/sklearn/preprocess.py example):

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docs/hyperdatasets/webapp/webapp_datasets_frames.md
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@ -3,7 +3,7 @@ title: Working with Frames
---
View and edit SingleFrames in the Dataset page. After selecting a Hyper-Dataset version, the **Version Browser** shows a sample
of frames and enables viewing SingleFrames and FramesGroups, and edit SingleFrames, in the [frame viewer](#frame-viewer).
of frames and enables viewing SingleFrames and FramesGroups, and editing SingleFrames, in the [frame viewer](#frame-viewer).
Before opening the frame viewer, you can filter the frames by applying [simple](#simple-frame-filtering) or [advanced](#advanced-frame-filtering)
filtering logic.