Small edits (#1031)

docs/getting_started/ds/best_practices.md

@@ -27,7 +27,7 @@ The goal of this phase is to get a code, dataset, and environment set up, so you
 - [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out [Getting Started](ds_first_steps.md)). 
   This helps visualizing the results and tracking progress.
 - [ClearML Agent](../../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time, 
-  while also creating an easy queue interface that easily lets you drop your experiments to be executed one by one
+  while also creating an easy queue interface that easily lets you drop your tasks to be executed one by one
   (great for ensuring that the GPUs are churning during the weekend).
 - [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, in the same way that you'd develop on your local laptop!
 
@@ -38,7 +38,7 @@ yields the best performing model for your task!
 
   - The real training (usually) should **not** be executed on your development machine.
   - Training sessions should be launched and monitored from a web UI.
-  - You should continue coding while experiments are being executed without interrupting them.
+  - You should continue coding while tasks are being executed without interrupting them.
   - Stop optimizing your code because your machine struggles, and run it on a beefier machine (cloud / on-prem).
 
 Visualization and comparison dashboards keep your sanity at bay! At this stage you usually have a docker container with all the binaries 
@@ -58,23 +58,23 @@ that you need.
 Track everything--from obscure parameters to weird metrics, it's impossible to know what will end up
 improving your results later on!
 
-- Make sure experiments are reproducible! ClearML logs code, parameters, and environment in a single, easily searchable place. 
+- Make sure tasks are reproducible! ClearML logs code, parameters, and environment in a single, easily searchable place. 
 - Development is not linear. Configuration / Parameters should not be stored in your git, as
   they are temporary and constantly changing. They still need to be logged because who knows, one day...
 - Uncommitted changes to your code should be stored for later forensics in case that magic number actually saved the day. Not every line change should be committed.
-- Mark potentially good experiments, make them the new baseline for comparison.
+- Mark potentially good tasks, make them the new baseline for comparison.
 
 ## Visibility Matters
 
-While you can track experiments with one tool, and pipeline them with another, having 
+While you can track tasks 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 task progress and compare tasks, and, based on that, send tasks 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, 
+Being able to have visibility in your pipeline, while using tasks already defined in the platform, 
 enables users to have a clearer picture of the pipeline's status 
 and makes it easier to start using pipelines earlier in the process by simplifying chaining tasks.
 
-Managing datasets with the same tools and APIs that manage the experiments also lowers the barrier of entry into 
-experiment and data provenance.
+Managing datasets with the same tools and APIs that manage the tasks also lowers the barrier of entry into 
+task and data provenance.

docs/getting_started/ds/ds_first_steps.md

@@ -99,7 +99,7 @@ Now you can use ClearML in your notebook!
 
 In ClearML, experiments are organized as [Tasks](../../fundamentals/task.md).
 
-ClearML automatically logs your experiment and code, including outputs and parameters from popular ML frameworks,
+ClearML automatically logs your task and code, including outputs and parameters from popular ML frameworks,
 once you integrate the ClearML [SDK](../../clearml_sdk/clearml_sdk.md) with your code. To control what ClearML automatically logs, see this [FAQ](../../faq.md#controlling_logging).
 
 At the beginning of your code, import the `clearml` package:
@@ -115,7 +115,7 @@ To ensure full automatic logging, it is recommended to import the `clearml` pack
 Then initialize the Task object in your `main()` function, or the beginning of the script.
 
 ```python
-task = Task.init(project_name='great project', task_name='best experiment')
+task = Task.init(project_name='great project', task_name='best task')
 ```
 
 If the project does not already exist, a new one is created automatically.

docs/getting_started/ds/ds_second_steps.md

@@ -151,14 +151,14 @@ Once everything is neatly logged and displayed, use the [comparison tool](../../
 
 ## Track Experiments
 
-The experiments table is a powerful tool for creating dashboards and views of your own projects, your team's projects, or the entire development.
+The task table is a powerful tool for creating dashboards and views of your own projects, your team's projects, or the entire development.
 
-![Experiments table](../../img/webapp_experiment_table.png#light-mode-only)
-![Experiments table](../../img/webapp_experiment_table_dark.png#dark-mode-only)
+![Task table](../../img/webapp_experiment_table.png#light-mode-only)
+![Task table](../../img/webapp_experiment_table_dark.png#dark-mode-only)
 
 
 ### Creating Leaderboards
-Customize the [experiments table](../../webapp/webapp_exp_table.md) to fit your own needs, adding desired views of parameters, metrics, and tags.
+Customize the [task table](../../webapp/webapp_exp_table.md) to fit your own needs, adding desired views of parameters, metrics, and tags.
 You can filter and sort based on parameters and metrics, so creating custom views is simple and flexible.
 
 Create a dashboard for a project, presenting the latest Models and their accuracy scores, for immediate insights.