Small edits (#636)

docs/clearml_serving/clearml_serving.md

@@ -9,7 +9,7 @@ solution.
 
 ## Features
 
-* Easy to deploy & configure
+* Easy to deploy and configure
     * Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
     * Support Deep Learning Models (TensorFlow, PyTorch, ONNX)
     * Customizable RestAPI for serving (i.e. allow per model pre/post-processing for easy integration)
@@ -25,7 +25,7 @@ solution.
     * Out-of-the-box node autoscaling based on load/usage
 * Efficient
     * Multi-container resource utilization
-    * Support for CPU & GPU nodes
+    * Support for CPU and GPU nodes
     * Auto-batching for DL models
 * [Automatic deployment](clearml_serving_tutorial.md#automatic-model-deployment)
     * Automatic model upgrades w/ canary support
@@ -52,7 +52,7 @@ solution.
 * **Serving Engine Services** - Inference engine containers (e.g. Nvidia Triton, TorchServe etc.) used by the Inference 
   Services for heavier model inference.
 
-* **Statistics Service** - Single instance per Serving Service collecting and broadcasting model serving & performance 
+* **Statistics Service** - Single instance per Serving Service collecting and broadcasting model serving and performance 
   statistics
 
 * **Time-series DB** - Statistics collection service used by the Statistics Service, e.g. Prometheus

docs/clearml_serving/clearml_serving_cli.md

@@ -21,7 +21,7 @@ clearml-serving [-h] [--debug] [--yes] [--id ID] {list,create,metrics,config,mod
 
 |Name|Description|Optional|
 |---|---|---|
-|`--id`|Serving Service (Control plane) Task ID to configure (if not provided automatically detect the running control plane Task) | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
+|`--id`|Serving Service (Control plane) Task ID to configure (if not provided, automatically detect the running control plane Task) | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
 |`--debug` |  Print debug messages | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
 |`--yes` |Always answer YES on interactive inputs| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
 

docs/clearml_serving/clearml_serving_setup.md

@@ -8,7 +8,7 @@ The following page goes over how to set up and upgrade `clearml-serving`.
 
 * ClearML-Server : Model repository, Service Health, Control plane
 * Kubernetes / Single-instance Machine : Deploying containers
-* CLI : Configuration & model deployment interface
+* CLI : Configuration and model deployment interface
 
 ## Initial Setup
 1. Set up your [ClearML Server](../deploying_clearml/clearml_server.md) or use the 

docs/clearml_serving/clearml_serving_tutorial.md

@@ -26,7 +26,7 @@ Train a model. Work from your local `clearml-serving` repository's root.
     `python3 examples/sklearn/train_model.py`. 
   
 During execution, ClearML automatically registers the sklearn model and uploads it into the model repository. 
-For Manual model registration see [here](#registering--deploying-new-models-manually) 
+For Manual model registration see [here](#registering-and-deploying-new-models-manually) 
 
 ### Step 2: Register Model
 
@@ -79,7 +79,7 @@ Inference services status, console outputs and machine metrics are available in
 project (default: "DevOps" project)
 :::
 
-## Registering & Deploying New Models Manually 
+## Registering and Deploying New Models Manually 
 
 Uploading an existing model file into the model repository can be done via the `clearml` RestAPI, the python interface, 
 or with the `clearml-serving` CLI. 
@@ -196,7 +196,7 @@ ClearML serving instances send serving statistics (count/latency) automatically
 to visualize and create live dashboards. 
 
 The default docker-compose installation is preconfigured with Prometheus and Grafana. Notice that by default data/ate 
-of both containers is *not* persistent. To add persistence, we recommend adding a volume mount.
+of both containers is *not* persistent. To add persistence, adding a volume mount is recommended.
 
 You can also add many custom metrics on the input/predictions of your models. Once a model endpoint is registered, 
 adding custom metrics can be done using the CLI.