Small edits (#722)

docs/clearml_sdk/task_sdk.md

@@ -450,8 +450,7 @@ queue. From there, an agent can pull and launch it.
 See the [Remote Execution](../guides/advanced/execute_remotely.md) example. 
 
 #### Remote Function Execution
-A specific function can also be launched on a remote machine with the [`Task.create_function_task`](../references/sdk/task.md#create_function_task)
-method.
+A specific function can also be launched on a remote machine with [`Task.create_function_task()`](../references/sdk/task.md#create_function_task).
 
 For example:
 ```python
@@ -467,7 +466,7 @@ a_func_task = task.create_function_task(
 )
 ```
 Arguments passed to the function will be automatically logged in the 
-experiment's **CONFIGURATION** tab under the **HYPERPARAMETER > Function** section. 
+experiment's **CONFIGURATION** tab under the **HYPERPARAMETERS > Function** section. 
 Like any other arguments, they can be changed from the UI or programmatically.
 
 :::note Function Task Creation

docs/faq.md

@@ -649,7 +649,7 @@ logger.report_scatter2d(
 #### Is there something ClearML can do about uncommitted code running?    <a id="help-uncommitted-code"></a>
 
 Yes! ClearML stores the git diff as part of the experiment's information. You can view the git diff in the **ClearML Web UI >** 
-experiment' **EXECUTION** tab.
+experiment's **EXECUTION** tab.
 
 <br/>
 

docs/getting_started/ds/best_practices.md

@@ -69,7 +69,7 @@ improving your results later on!
 While it's possible to track experiments with one tool, and pipeline them with another, having 
 everything under the same roof has its benefits! 
 
-Being able to track experiment progress and compare experiments, and based on that send experiments to execution on remote
+Being able to track experiment progress and compare experiments, and, based on that, send experiments to execution on remote
 machines (that also build the environment themselves) has tremendous benefits in terms of visibility and ease of integration.
 
 Being able to have visibility in your pipeline, while using experiments already defined in the platform, 

docs/getting_started/mlops/mlops_first_steps.md

@@ -55,7 +55,7 @@ required python packages, and execute and monitor the process.
 :::tip Agent Deployment Modes
 ClearML Agents can be deployed in Virtual Environment Mode or Docker Mode. In [virtual environment mode](../../clearml_agent.md#execution-environments), 
 the agent creates a new venv to execute an experiment. In [Docker mode](../../clearml_agent.md#docker-mode), 
-the agent executes an experiment inside a Docker container. See all running mode options [here](../../fundamentals/agents_and_queues.md#additional-features).  
+the agent executes an experiment inside a Docker container. For more information, see [Running Modes](../../fundamentals/agents_and_queues.md#running-modes).  
 :::
 
 ## Clone an Experiment

docs/getting_started/mlops/mlops_second_steps.md

@@ -8,9 +8,9 @@ Pipelines provide users with a greater level of abstraction and automation, with
 Tasks can interface with other Tasks in the pipeline and leverage other Tasks' work products.
 
 The sections below describe the following scenarios: 
-* Dataset creation
-* Data processing and consumption  
-* Pipeline building
+* [Dataset creation](#dataset-creation)
+* Data [processing](#preprocessing-data) and [consumption](#training)  
+* [Pipeline building](#building-the-pipeline)
 
 
 ## Building Tasks
@@ -46,7 +46,8 @@ dataset_folder = dataset.get_mutable_local_copy(
 
 # create a new version of the dataset with the pickle file
 new_dataset = Dataset.create(
-    dataset_project='data', dataset_name='dataset_v2', 
+    dataset_project='data', 
+    dataset_name='dataset_v2', 
     parent_datasets=[dataset], 
     use_current_task=True,  
     # this will make sure we have the creation code and the actual dataset artifacts on the same Task

docs/integrations/monai.md

@@ -80,7 +80,7 @@ View the logged metrics in the WebApp, in the experiment's **Scalars** tab.
 
 ClearML automatically logs models saved using the `ModelCheckpoint` handler. Make sure a ClearML Task is instantiated in
 your script. If you're already using either `ClearMLStatsHandler` or `ClearMLImageHandler`, you don't have to add any code. 
-Otherwise, all you have to is add two lines of code to create a task:
+Otherwise, all you have to do is add two lines of code to create a task:
 
 ```python
 from clearml import Task

docs/pipelines/pipelines_sdk_tasks.md

@@ -96,13 +96,13 @@ pipe.add_step(
 * `cache_executed_step` – If `True`, the controller will check if an identical task with the same code (including setup, 
   e.g. required packages, docker image, etc.) and input arguments was already executed. If found, the cached step's 
   outputs are used instead of launching a new task.
-* `execution_queue` (optional) - the queue to use for executing this specific step. If not provided, the task will be sent to the default execution queue, as defined on the class
-* `parents` – Optional list of parent steps in the pipeline. The current step in the pipeline will be sent for execution only after all the parent steps have been executed successfully.
+* `execution_queue` (optional) - The queue to use for executing this specific step. If not provided, the task will be sent to the default execution queue, as defined on the class.
+* `parents` (optional) - List of parent steps in the pipeline. The current step in the pipeline will be sent for execution only after all the parent steps have been executed successfully.
 * `parameter_override` - Dictionary of parameters and values to override in the current step. See [parameter_override](#parameter_override).
-* `configuration_overrides` - Dictionary of configuration objects and values to override in the current step. See [configuration_overrides](#configuration_overrides)
+* `configuration_overrides` - Dictionary of configuration objects and values to override in the current step. See [configuration_overrides](#configuration_overrides).
 * `monitor_models`, `monitor_metrics`, `monitor_artifacts` - see [here](#models-artifacts-and-metrics).
 
-See [add_step](../references/sdk/automation_controller_pipelinecontroller.md#add_step) for all arguments.
+See [`PipelineController.add_step`](../references/sdk/automation_controller_pipelinecontroller.md#add_step) for all arguments.
 
 #### parameter_override
 Use the `parameter_override` argument to modify the step's parameter values. The `parameter_override` dictionary key is 
@@ -164,13 +164,13 @@ pipe.add_function_step(
   (including setup, see task [Execution](../webapp/webapp_exp_track_visual.md#execution) 
   section) and input arguments was already executed. If found, the cached step's 
   outputs are used instead of launching a new task.
-* `parents` – Optional list of parent steps in the pipeline. The current step in the pipeline will be sent for execution 
+* `parents` (optional) - List of parent steps in the pipeline. The current step in the pipeline will be sent for execution 
   only after all the parent steps have been executed successfully.
 * `pre_execute_callback` and `post_execute_callback` - Control pipeline flow with callback functions that can be called 
   before and/or after a step's execution. See [here](#pre_execute_callback-and-post_execute_callback).
 * `monitor_models`, `monitor_metrics`, `monitor_artifacts` - see [here](#models-artifacts-and-metrics).
 
-See [add_function_step](../references/sdk/automation_controller_pipelinecontroller.md#add_function_step) for all 
+See [`PipelineController.add_function_step`](../references/sdk/automation_controller_pipelinecontroller.md#add_function_step) for all 
 arguments.
 
 ### Important Arguments