From 59c8d343ead061eabf34f8bfc70d06edbe2c770d Mon Sep 17 00:00:00 2001
From: Allegro AI <51604379+allegroai-git@users.noreply.github.com>
Date: Sun, 6 Mar 2022 01:39:59 +0200
Subject: [PATCH] Update README.md

---
 README.md | 34 ++++++++++++++++++----------------
 1 file changed, 18 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index da9d6d3..7566ff7 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 
 **ClearML Serving - Model deployment made easy**
 
-## **`clearml-serving` </br> Model Serving (ML/DL) Orchestration and Repository Made Easy**
+## **`clearml-serving` </br> :sparkles: Model Serving (ML/DL) Made Easy :tada:**
 
 
 [![GitHub license](https://img.shields.io/github/license/allegroai/clearml-serving.svg)](https://img.shields.io/github/license/allegroai/clearml-serving.svg)
@@ -26,17 +26,17 @@ Features:
   * Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
   * Support Deep Learning Models (Tensorflow, PyTorch, ONNX)
   * Customizable RestAPI for serving (i.e. allow per model pre/post-processing for easy integration)
-* Flexibility
+* Flexible  
   * On-line model deployment 
   * On-line endpoint model/version deployment (i.e. no need to take the service down)
   * Per model standalone preprocessing and postprocessing python code 
-* Scalability
+* Scalable
   * Multi model per container
   * Multi models per serving service
   * Multi-service support (fully seperated multiple serving service running independently)
   * Multi cluster support
   * Out-of-the-box node auto-scaling based on load/usage
-* Efficiency
+* Efficient
   * multi-container resource utilization
   * Support for CPU & GPU nodes
   * Auto-batching for DL models
@@ -55,11 +55,7 @@ Features:
 
 ### ClearML Serving Design Principles 
 
-* Modular
-* Scalable
-* Flexible
-* Customizable
-* Open Source
+**Modular** , **Scalable** , **Flexible** , **Customizable** , **Open Source**
 
 <a href="https://excalidraw.com/#json=v0ip945hun2SnO4HVLe0h,QKHfB04TFQLds3_4aqeBjQ"><img src="https://github.com/allegroai/clearml-serving/blob/dev/docs/design_diagram.png?raw=true" width="100%"></a>
 
@@ -67,13 +63,19 @@ Features:
 
 ### Concepts
 
-CLI - Secure configuration interface for on-line model upgrade/deployment on running Serving Services
-Serving Service Task - Control plane object storing configuration on all the endpoints. Support multiple separated instance, deployed on multiple clusters.
-Inference Services - Inference containers, performing model serving pre/post processing. Also support CPU model inferencing.
-Serving Engine Services - Inference engine containers (e.g. Nvidia Triton, TorchServe etc.) used by the Inference Services for heavier model inference.
-Statistics Service - Single instance per Serving Service  collecting and broadcasting model serving & performance statistics
-Time-series DB - Statistics collection service used by the Statistics Service, e.g. Prometheus
-Dashboard Service - Customizable dashboard-ing solution on top of the collected statistics, e.g. Grafana
+**CLI** - Secure configuration interface for on-line model upgrade/deployment on running Serving Services
+
+**Serving Service Task** - Control plane object storing configuration on all the endpoints. Support multiple separated instance, deployed on multiple clusters.
+
+**Inference Services** - Inference containers, performing model serving pre/post processing. Also support CPU model inferencing.
+
+**Serving Engine Services** - Inference engine containers (e.g. Nvidia Triton, TorchServe etc.) used by the Inference Services for heavier model inference.
+
+**Statistics Service** - Single instance per Serving Service  collecting and broadcasting model serving & performance statistics
+
+**Time-series DB** - Statistics collection service used by the Statistics Service, e.g. Prometheus
+
+**Dashboards** - Customizable dashboard-ing solution on top of the collected statistics, e.g. Grafana
 
 ### prerequisites