Change headings to title caps (#62)

docs/getting_started/ds/ds_first_steps.md

@@ -5,6 +5,7 @@ title: First Steps
 
 ## Install ClearML
 
+
 First, [sign up for free](https://app.community.clear.ml)
 
 Install the clearml python package:
@@ -18,7 +19,7 @@ clearml-init
 ```
 
 
-## Auto-log experiment
+## Auto-log Experiment
 
 In ClearML, experiments are organized as [Tasks](../../fundamentals/task). 
 

docs/getting_started/ds/ds_second_steps.md

@@ -46,7 +46,7 @@ Artifacts can be stored anywhere, either on the ClearML server, or any object st
 see all [storage capabilities](../../integrations/storage).
 
 
-### Adding artifacts
+### Adding Artifacts
 
 Uploading a local file containing the preprocessed results of the data:
 ```python
@@ -154,7 +154,7 @@ Any page is sharable by copying the URL from the address bar, allowing you to bo
 It's also possible to tag Tasks for visibility and filtering allowing you to add more information on the execution of the experiment. 
 Later you can search based on task name and tag in the search bar, and filter experiments based on their tags, parameters, status and more.
 
-## What's next?
+## What's Next?
 
 This covers the Basics of ClearML! Running through this guide we've learned how to log Parameters, Artifacts and Metrics!
 

docs/getting_started/main.md

@@ -11,7 +11,7 @@ while ClearML ensures your work is reproducible and scalable.
 
 <img src="https://github.com/allegroai/clearml-docs/blob/main/docs/img/clearml_architecture.png?raw=true" width="100%" alt="Architecture diagram"/>
 
-## What can you do with ClearML?
+## What Can You Do with ClearML?
 
 - Track and upload metrics and models with only 2 lines of code
 - Create a bot that sends you a slack message whenever you model improves in accuracy

docs/getting_started/mlops/mlops_best_practices.md

@@ -28,7 +28,7 @@ Once we have a Task in ClearML, we can clone and edit its definition in the UI.
 - Create data monitoring & scheduling and launch inference jobs to test performance on any new coming dataset.
 - Once there are two or more experiments that run after another, group them together into a [pipeline](../../fundamentals/pipelines.md)
 
-## Manage your data
+## Manage Your Data
 Use [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

docs/getting_started/mlops/mlops_first_steps.md

@@ -123,7 +123,7 @@ from clearml import Task
 executed_task = Task.get_task(task_id='aabbcc')
 # get a summary of the min/max/last value of all reported scalars
 min_max_vlues = executed_task.get_last_scalar_metrics()
-# get detialed graphs of all scalars
+# get detailed graphs of all scalars
 full_scalars = executed_task.get_reported_scalars()
 ```