stylistic changes

Signed-off-by: Samhita Alla <[email protected]>

plugins/flytekit-aws-sagemaker/README.md

@@ -1,11 +1,13 @@
 # Flytekit AWS Sagemaker Plugin
 
-Flyte backend can be connected with Athena. Once enabled, it allows you to query AWS Athena service (Presto + ANSI SQL Support) and retrieve typed schema (optionally).
+Amazon SageMaker provides several built-in machine learning algorithms that you can use for a variety of problem types. Flyte Sagemaker plugin intends to greatly simplify using Sagemaker for training. We have tried to distill the API into a meaningful subset that makes it easier for users to adopt and run with Sagemaker.
 
 To install the plugin, run the following command:
 
 ```bash
-pip install flytekitplugins-athena
+pip install flytekitplugins-awssagemaker
 ```
 
-An [example](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/aws/athena/athena.html#sphx-glr-auto-integrations-aws-athena-athena-py) can be found in the documentation.
+To install Sagemaker in the Flyte deployment's backend, go through the [prerequisites](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/aws/sagemaker_training/index.html#prerequisites).
+
+[Built-in sagemaker](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/aws/sagemaker_training/sagemaker_builtin_algo_training.html#sphx-glr-auto-integrations-aws-sagemaker-training-sagemaker-builtin-algo-training-py) and [custom sagemaker](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/aws/sagemaker_training/sagemaker_custom_training.html#sphx-glr-auto-integrations-aws-sagemaker-training-sagemaker-custom-training-py) training models can be found in the documentation.

plugins/flytekit-dolt/README.md

@@ -2,7 +2,7 @@
 
 The DoltTable plugin is a wrapper that uses [Dolt](https://github.com/dolthub/dolt) to move data between pandas.DataFrame’s at execution time and database tables at rest.
 
-The dolt plugin and dolt command line tool are required to run these examples:
+The Dolt plugin and Dolt command-line tool can be installed as follows:
 
 ```bash
 pip install flytekitplugins.dolt
@@ -16,4 +16,4 @@ dolt config --global --add user.email <email>
 dolt config --global --add user.name <name>
 ```
 
-The [examples](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/flytekit_plugins/dolt/index.html) can be found in the documentation.
+All the [examples](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/flytekit_plugins/dolt/index.html) can be found in the documentation.

plugins/flytekit-hive/README.md

@@ -1,6 +1,6 @@
 # Flytekit Hive Plugin
 
-Flyte backend can be connected with various Hive services. Once enabled, it allows to query a Hive service (e.g., Qubole) and retrieve typed schema (optionally).
+Flyte backend can be connected with various Hive services. Once enabled, it allows you to query a Hive service (e.g., Qubole) and retrieve typed schema (optionally).
 
 To install the plugin, run the following command:
 

plugins/flytekit-k8s-pod/README.md

@@ -1,8 +1,8 @@
 # Flytekit Kubernetes Pod Plugin
 
-By default, Flyte tasks decorated with @task are essentially single functions that are loaded in one container. But often, there is a need to run a job with more than one container.
+By default, Flyte tasks decorated with `@task` are essentially single functions that are loaded in one container. But often, there is a need to run a job with more than one container.
 
-In this case, a regular task is not enough. Hence, Flyte provides a Kubernetes pod abstraction to execute multiple containers, which can be done using Pod's `task_config`. The `task_config` can be leveraged to fully customize the pod spec used to run the task.
+In this case, a regular task is not enough. Hence, Flyte provides a Kubernetes pod abstraction to execute multiple containers, which can be accomplished using Pod's `task_config`. The `task_config` can be leveraged to fully customize the pod spec used to run the task.
 
 To install the plugin, run the following command:
 

plugins/flytekit-kf-tensorflow/README.md

@@ -7,3 +7,5 @@ To install the plugin, run the following command:
 ```bash
 pip install flytekitplugins-kftensorflow
 ```
+
+_Example coming soon!_

plugins/flytekit-modin/README.md

@@ -1,8 +1,11 @@
 # Flytekit Modin Plugin
+
 Modin is an emerging drop-in replacement or rather extension of Pandas. This plugin could be helpful to use Modin as a data type.
 
 To install the plugin, run the following command:
 
 ```bash
 pip install flytekitplugins-modin
 ```
+
+_Example coming soon!_

plugins/flytekit-papermill/README.md

@@ -8,4 +8,4 @@ To install the plugin, run the following command:
 pip install flytekitplugins-papermill
 ```
 
-An [example](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/flytekit_plugins/papermilltasks/simple.html#sphx-glr-auto-integrations-flytekit-plugins-papermilltasks-simple-py) can be found in the documentation. We have also written a tutorial showcasing the various ways in which you can leverage the Papermill plugin. Refer to the [tutorials section](https://docs.flyte.org/projects/cookbook/en/latest/auto/case_studies/feature_engineering/eda/index.html) to learn more about it.
+An [example](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/flytekit_plugins/papermilltasks/simple.html#sphx-glr-auto-integrations-flytekit-plugins-papermilltasks-simple-py) can be found in the documentation. We have also a [tutorial](https://docs.flyte.org/projects/cookbook/en/latest/auto/case_studies/feature_engineering/eda/index.html) showcasing the various ways in which you can leverage the Papermill plugin.

plugins/flytekit-snowflake/README.md

@@ -1,6 +1,6 @@
 # Flytekit Snowflake Plugin
 
-Snowflake enables us to build data-intensive applications without operational burden. Flyte backend can be connected with the Snowflake service. Once enabled, it can allow us to query a Snowflake service.
+Snowflake enables us to build data-intensive applications without operational burden. Flyte backend can be connected with the Snowflake service. Once enabled, it can allow you to query a Snowflake service.
 
 To install the plugin, run the following command:
 

plugins/flytekit-spark/README.md

@@ -10,4 +10,4 @@ pip install flytekitplugins-spark
 
 To configure Spark in the Flyte deployment's backend, follow [Step 1](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/kubernetes/k8s_spark/index.html#step-1-deploy-spark-plugin-in-the-flyte-backend), [2](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/kubernetes/k8s_spark/index.html#step-2-environment-setup), and [3](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/kubernetes/k8s_spark/index.html#step-3-optionally-setup-visibility).
 
-All [examples](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/kubernetes/k8s_spark/index.html) showcasing execution of Spark jobs can be found in the documentation.
+All [examples](https://docs.flyte.org/projects/cookbook/en/latest/auto/integrations/kubernetes/k8s_spark/index.html) showcasing execution of Spark jobs using the plugin can be found in the documentation.