Small edits (#717)

README.md

@@ -13,11 +13,11 @@
 The [ClearML documentation website](https://clear.ml/docs/latest/docs) is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator.
 
 
-## Contributing (yes please!)
+## Contributing (yes, please!)
 
 **PRs are always welcomed** :heart:
 
-Good PR examples
+Good PR examples:
 * If you see something that is inaccurate or missing
 * A topic that interests you is not addressed
 * You feel that a guide would have made your life easier

docs/getting_started/mlops/mlops_best_practices.md

@@ -31,7 +31,7 @@ Once you have a Task in ClearML, you can clone and edit its definitions in the U
 ## Manage Your Data
 Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running experiments for easy reproduction.
 Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder / S3 / Gs / Azure).
-ClearML Data supports efficient Dataset storage and caching, differentiable & compressed.
+ClearML Data supports efficient Dataset storage and caching, differentiable and compressed.
 
 ## Scale Your Work
 Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (remote or local) and manage

docs/getting_started/mlops/mlops_first_steps.md

@@ -21,7 +21,7 @@ ClearML Agent was designed to deal with such issues and more! It is a tool respo
 machine of choice through the ClearML WebApp with no need for additional code.
 
 The agent will set up the environment for a specific Task’s execution (inside a Docker, or bare-metal), install the 
-required python packages, and execute & monitor the process.
+required python packages, and execute and monitor the process.
 
 
 ## Set up an Agent

docs/guides/clearml_agent/reproduce_exp.md

@@ -24,7 +24,7 @@ You can reproduce the execution environment of any experiment you’ve run with
 
 1. Go to the experiment page of the task you want to reproduce in the [ClearML WebApp](../../webapp/webapp_overview.md), 
    :::tip
-   Use the UI's [filtering and sorting](../../webapp/webapp_exp_table.md#filtering-columns) to find the best performing experiments  
+   Use the UI's [filtering and sorting](../../webapp/webapp_exp_table.md#filtering-columns) to find the best performing experiments.  
    ::: 
 1. Copy the desired experiment's ID
 1. Use the ClearML Agent's [`build`](../../clearml_agent/clearml_agent_ref.md#build) command to rebuild the experiment's

docs/integrations/monai.md

@@ -1,5 +1,5 @@
 ---
-title: Monai
+title: MONAI
 ---
 
 :::tip
@@ -7,8 +7,8 @@ If you are not already using ClearML, see [Getting Started](../getting_started/d
 instructions.
 :::
 
-[Monai](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare 
-imaging. You can integrate ClearML into your code using Monai's built-in handlers: [`ClearMLImageHandler`, `ClearMLStatsHandler`](#clearmlimagehandler-and-clearmlstatshandler), 
+[MONAI](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare 
+imaging. You can integrate ClearML into your code using MONAI's built-in handlers: [`ClearMLImageHandler`, `ClearMLStatsHandler`](#clearmlimagehandler-and-clearmlstatshandler), 
 and [`ModelCheckpoint`](#modelcheckpoint). 
 
 ## ClearMLImageHandler and ClearMLStatsHandler