Small edits (#451)

docs/getting_started/ds/ds_second_steps.md

@@ -24,7 +24,7 @@ Once we have a Task object we can query the state of the Task, get its Model, sc
 ## Log Hyperparameters
 
 For full reproducibility, it's paramount to save Hyperparameters for each experiment. Since Hyperparameters can have substantial impact
-on Model performance, saving and comparing these between experiments is sometimes the key to understand model behavior.
+on Model performance, saving and comparing these between experiments is sometimes the key to understanding model behavior.
 
 ClearML supports logging `argparse` module arguments out of the box, so once ClearML is integrated into the code, it automatically logs all parameters provided to the argument parser.
 
@@ -93,8 +93,8 @@ Check out the [artifacts retrieval](https://github.com/allegroai/clearml/blob/ma
 
 ### Models
 
-Models are a special kind artifact.
-Models created by popular frameworks (such as Pytorch, Tensorflow, Scikit-learn) are automatically logged by ClearML.
+Models are a special kind of artifact.
+Models created by popular frameworks (such as PyTorch, TensorFlow, Scikit-learn) are automatically logged by ClearML.
 All snapshots are automatically logged. In order to make sure we also automatically upload the model snapshot (instead of saving its local path),
 we need to pass a storage location for the model files to be uploaded to.
 
@@ -107,11 +107,12 @@ task = Task.init(
 )
 ```
 
-Now, whenever the framework (TF/Keras/PyTorch etc.) stores a snapshot, the model file is automatically uploaded to the bucket to a specific folder for the experiment.
+Now, whenever the framework (TensorFlow/Keras/PyTorch etc.) stores a snapshot, the model file is automatically uploaded to the bucket to a specific folder for the experiment.
 
-Loading models by a framework is also logged by the system, these models appear under the “Input Models” section, under the Artifacts tab.
+Loading models by a framework is also logged by the system; these models appear in an experiment's **Artifacts** tab,
+under the "Input Models" section.
 
-Check out model snapshots examples for [TF](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py),
+Check out model snapshots examples for [TensorFlow](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py),
 [PyTorch](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py),
 [Keras](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py),
 [Scikit-Learn](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py).
@@ -127,7 +128,7 @@ local_weights_path = last_snapshot.get_local_copy()
 
 Like before we have to get the instance of the Task training the original weights files, then we can query the task for its output models (a list of snapshots), and get the latest snapshot.
 :::note
-Using Tensorflow, the snapshots are stored in a folder, meaning the `local_weights_path` will point to a folder containing our requested snapshot.
+Using TensorFlow, the snapshots are stored in a folder, meaning the `local_weights_path` will point to a folder containing our requested snapshot.
 :::
 As with Artifacts, all models are cached, meaning the next time we run this code, no model needs to be downloaded.
 Once one of the frameworks will load the weights file, the running Task will be automatically updated with “Input Model” pointing directly to the original training Task’s Model.

docs/getting_started/mlops/mlops_first_steps.md

@@ -162,7 +162,7 @@ and [pipeline](../../pipelines/pipelines.md) solutions.
 
 #### Log Models
 Logging models into the model repository is the easiest way to integrate the development process directly with production. 
-Any model stored by a supported framework (Keras / TF / PyTorch / Joblib etc.) will be automatically logged into ClearML.
+Any model stored by a supported framework (Keras / TensorFlow / PyTorch / Joblib etc.) will be automatically logged into ClearML.
 
 ClearML also offers methods to explicitly log models. Models can be automatically stored on a preferred storage medium 
 (s3 bucket, google storage, etc.). 

docs/getting_started/video_tutorials/hyperdatasets_data_versioning.md

@@ -1,6 +1,6 @@
 ---
-title: Hyperdatasets Data Versioning
-description: Learn more about the hyperdatasets, a supercharged version of ClearML Data.
+title: Hyper-Datasets Data Versioning
+description: Learn more about the Hyper-Datasets, a supercharged version of ClearML Data.
 keywords: [mlops, components, hyperdatasets]
 ---
 
@@ -21,11 +21,11 @@ keywords: [mlops, components, hyperdatasets]
 <summary className="cml-expansion-panel-summary">Read the transcript</summary>
 <div className="cml-expansion-panel-content">
 
-Hello and welcome to ClearML. In this video, we're taking a closer look at hyperdatasets, a supercharged version of ClearML Data.
+Hello and welcome to ClearML. In this video, we're taking a closer look at Hyper-Datasets, a supercharged version of ClearML Data.
 
-Hyperdatasets is a data management system that’s designed for unstructured data like text, audio, or visual data. It is part of the ClearML paid offering, which means it brings along quite a bit of upgrades over the open source `clearml-data`.
+Hyper-Datasets is a data management system that’s designed for unstructured data like text, audio, or visual data. It is part of the ClearML paid offering, which means it brings along quite a bit of upgrades over the open source `clearml-data`.
 
-The main conceptual difference between the two is that hyperdatasets decouple the metadata from the raw data files. This allows you to manipulate the metadata in all kinds of ways while abstracting away the logistics of having to deal with large amounts of data. 
+The main conceptual difference between the two is that Hyper-Datasets decouple the metadata from the raw data files. This allows you to manipulate the metadata in all kinds of ways while abstracting away the logistics of having to deal with large amounts of data. 
 
 Manipulating the metadata is done through queries and parameters, both of which can then be tracked using the experiment manager. 
 
@@ -35,9 +35,9 @@ The data manipulations themselves become part of the experiment, we call it a da
 
 By contrast, in ClearML Data, just like many other data versioning tools, the data and the metadata are entangled. Take this example where the label of the image is defined by which folder it is in, a common dataset structure. What if I want to train only on donuts? Or what if I have a large class imbalance? I still have to download the whole dataset even though I might only be using a small part of it. Then I have to change my code to only grab the donut images or to rebalance my classes by over or under sampling them. If later I want to add waffles to the mix, I have to change my code again. 
 
-Let’s take a look at an example that will show you how to use hyperdatasets to debug an underperforming model. But first, we start where any good data science projects starts: data exploration.
+Let’s take a look at an example that will show you how to use Hyper-Datasets to debug an underperforming model. But first, we start where any good data science projects starts: data exploration.
 
-When you open hyperdatasets to explore a dataset, you can find the version history of that dataset here. Datasets can have multiple versions, which in turn can have multiple child versions. Each of the child versions will inherit the contents of their parents.
+When you open Hyper-Datasets to explore a dataset, you can find the version history of that dataset here. Datasets can have multiple versions, which in turn can have multiple child versions. Each of the child versions will inherit the contents of their parents.
 
 By default, a dataset version will be in draft mode, meaning it can still be modified. You can press the publish button to essentially lock it to make sure it will not change anymore. If you want to make changes to a published dataset version, make a new version that’s based on it.
 
@@ -53,13 +53,13 @@ The goal of these queries is not to simply serve as a neat filter for data explo
 
 Enter the dataviews that I introduced in the beginning of this video. Dataviews can use sophisticated queries to connect specific data from one or more datasets to an experiment in the experiment manager. Essentially it creates and manages local views of remote Datasets.
 
-As an example, imagine you have created an experiment that tries to train a model based on a specific subset of data using hyperdatasets.
+As an example, imagine you have created an experiment that tries to train a model based on a specific subset of data using Hyper-Datasets.
 
 To get the data you need to train on, you can easily create a dataview from code like so. Then you can add all sorts of constraints, like class filters, metadata filters, and class weights which will over or under sample the data as is required.
 
-After running the task, we can see it in the experiment manager. The model is reporting scalars and training as we would expect. When using hyperdatasets, there is also a dataviews tab with all of the possibilities at your disposal. You can see which input datasets and versions that you used and can see the querying system that is used to subset them. This will already give you a nice, clean way to train your models on a very specific subset of the data, but there is more!
+After running the task, we can see it in the experiment manager. The model is reporting scalars and training as we would expect. When using Hyper-Datasets, there is also a dataviews tab with all of the possibilities at your disposal. You can see which input datasets and versions that you used and can see the querying system that is used to subset them. This will already give you a nice, clean way to train your models on a very specific subset of the data, but there is more!
 
-If you want to remap labels, or enumerate them to integers on-the-fly, ClearML will keep track of all the transformations that are done and make sure they are reproducible. There is, of course, more still, so if you’re interested check out our documentation on hyperdatasets.
+If you want to remap labels, or enumerate them to integers on-the-fly, ClearML will keep track of all the transformations that are done and make sure they are reproducible. There is, of course, more still, so if you’re interested check out our documentation on Hyper-Datasets.
 
 ClearML veterans already know what’s coming next. Cloning.
 
@@ -73,6 +73,6 @@ After the remote machine has executed the experiment on the new dataview, we can
 
 If you’ve been following along with the other Getting Started videos, you should already start to see the potential this approach can have. For example: we could now run hyperparameter optimization on the data itself, because all of the filters and settings previously shown are just parameters on a task. The whole process could be running in parallel on a cloud autoscaler for example. Imagine finding the best training data confidence threshold for each class to optimize the model performance.
 
-If you’re interested in using Hyperdatasets for your team, then contact us using our website and we’ll get you going in no time. In the meantime, you can enjoy the power of the open source components at app.clear.ml, and don’t forget to join our Slack channel, if you need any help!
+If you’re interested in using Hyper-Datasets for your team, then contact us using our website and we’ll get you going in no time. In the meantime, you can enjoy the power of the open source components at app.clear.ml, and don’t forget to join our Slack channel, if you need any help!
 </div>
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