docs: slightly revised etl pic and caps

NVIDIA · Dec 10, 2020 · be29c5a · be29c5a
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diff --git a/deploy/dev/docker/README.md b/deploy/dev/docker/README.md
@@ -227,13 +227,13 @@ Refer to the `logs.sh` script for more details about its usage.
 
 ### `get_ip_addresses.sh`
 
-If you want to quickly get the ip addresses of each container of all clusters, use the following script:
+If you'd want to quickly get IP addresses of all running containers, execute the following:
 
 ```console
 $ ./get_ip_addresses.sh
 ```
 
-* Note: The port numbers for the ip addresses for each container will be 51080, 9080, 10080 by default for the public, intra control and intra data networks respectively.
+> The default port numbers will be `51080`, `9080`, and `10080` respectively for the **public**, **intra-control** and **intra-data** networks.
 
 ### Prune docker
 

diff --git a/docs/etl.md b/docs/etl.md
@@ -1,5 +1,9 @@
 # Extract, Transform, Load (ETL) with AIStore
 
+**New** in v3.3: offline transformation and the capability to run user-defined functions (in addition to custom ETL containers). Also, bug fixes, performance improvements across the board, video and GIF presentations, and more.
+
+<img src="images/etl-v3.3.png" width="450">
+
 - [Introduction](#introduction)
 - [Getting Started](#getting-started)
 - [Inline ETL example](#inline-etl-example)

diff --git a/docs/getting_started.md b/docs/getting_started.md
@@ -1,6 +1,6 @@
 # Getting Started
 
-You could start running AIS on a single Linux or Mac host. Alternatively, or in addition, you could quickly deploy your first AIS cluster on [GCP/GKE](https://cloud.google.com/kubernetes-engine). This document introduces these and other supported options and provides quick links for further information.
+You could run AIS on a single Linux or Mac machine. Or, you could quickly deploy your first AIS cluster on [GCP/GKE](https://cloud.google.com/kubernetes-engine) cloud. This document introduces these and other supported options and provides quick links for further information.
 
 Generally, when deciding how to deploy a system like AIS with so many options to choose from, a good place to start would be answering the following two fundamental questions:
 

diff --git a/docs/images/etl-v3.3.png b/docs/images/etl-v3.3.png
diff --git a/docs/overview.md b/docs/overview.md
@@ -35,12 +35,12 @@ Following is a high-level architecture with an emphasis on supported (frontend a
 
 <img src="images/ais-block.png" alt="New in 3.1" width="700">
 
-Most recently capabilities include:
+Most recently added capabilities include:
 
 - (**new**) [ETL offload](/etl/README.md): support for running custom extract-transform-load workloads on (and by) storage cluster;
 - (**new**) TensorFlow integration to support existing training clients that use S3 API - done via `tar2tf` ETL offload that handles on the fly TFRecord/tf.Example conversion;
 
-<img src="images/etl-v3.2.png" alt="ETL 1.0" width="450">
+<img src="images/etl-v3.3.png" width="450">
 
 - List objects v2: optimized `list-objects` to greatly reduce response times;
 - (**new**) Query objects: extends `list-objects` with advanced filtering capabilities;