Small edits (#128)

docs/getting_started/architecture.md

@@ -3,10 +3,10 @@ title: ClearML Modules
 ---
 
 - **ClearML Python Package** (clearml) for integrating **ClearML** into your existing code-base.
-- **ClearML Server** (clearml-server) storing experiment, model, and workflow data, and supporting the Web UI experiment manager. It is also the control plane for the ML-Ops.
-- **ClearML Agent** (clearml-agent) The ML-Ops orchestration agent. Enabling experiment and workflow reproducibility, and scalability.
+- **ClearML Server** (clearml-server) storing experiment, model, and workflow data, and supporting the Web UI experiment manager. It is also the control plane for the MLOps.
+- **ClearML Agent** (clearml-agent) The MLOps orchestration agent. Enabling experiment and workflow reproducibility, and scalability.
 - **ClearML Data** (clearml-data) data management and versioning on top of file-systems/object-storage.
 - **ClearML Session** (clearml-session) Launch remote instances of Jupyter Notebooks and VSCode.
-solutions combined with the clearml-server control plain.
+Solutions combined with the clearml-server control plane.
 
 ![clearml architecture](../img/clearml_architecture.png)

docs/getting_started/main.md

@@ -14,10 +14,10 @@ while ClearML ensures your work is reproducible and scalable.
 ## 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
+- Create a bot that sends you a Slack message whenever your model improves in accuracy
 - Automatically scale AWS instances according to your resources needs
 - Reproduce experiments with 3 mouse clicks
-- Much More!
+- Much more!
 
 #### Who We Are
 ClearML is supported by you :heart: and by the team behind [allegro.ai](https://www.allegro.ai) , where we build even more MLOps for enterprise companies.

docs/getting_started/mlops/mlops_first_steps.md

@@ -72,7 +72,7 @@ Docker container image to be used, or change the hyperparameters and configurati
 Once you have set up an experiment, it is now time to execute it. 
 
 **To execute an experiment through the ClearML WebApp:**
-1. Right click your draft experiment (the context menu is also available through the <img src="/docs/latest/icons/ico-bars-menu.svg" className="icon size-md space-sm" /> 
+1. Right click your draft experiment (the context menu is also available through the <img src="/docs/latest/icons/ico-bars-menu.svg" alt="Menu" className="icon size-md space-sm" /> 
    button on the top right of the experiment’s info panel)
 1. Click **ENQUEUE,** which will open the **ENQUEUE EXPERIMENT** window
 1. In the window, select `default` in the queue menu

docs/getting_started/mlops/mlops_second_steps.md

@@ -35,14 +35,18 @@ task = Task.init(project_name='data', task_name='create', task_type='data_proces
 dataset = Dataset.get(dataset_project='data', dataset_name='dataset_v1')
 
 # get a local mutable copy of the dataset
-dataset_folder = dataset.get_mutable_local_copy(target_folder='work_dataset', overwrite=True)
+dataset_folder = dataset.get_mutable_local_copy(
+    target_folder='work_dataset', 
+    overwrite=True
+)
 # change some files in the `./work_dataset` folder
 ...
 # create a new version of the dataset with the pickle file
 new_dataset = Dataset.create(
     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
+    use_current_task=True,  
+    # this will make sure we have the creation code and the actual dataset artifacts on the same Task
 )
 new_dataset.sync_folder(local_path=dataset_folder)
 new_dataset.upload()