From 75f5814f9f316ba3322ef4574f83ed417c5ff195 Mon Sep 17 00:00:00 2001
From: Allegro AI <51604379+allegroai-git@users.noreply.github.com>
Date: Wed, 19 Oct 2022 02:44:53 +0300
Subject: [PATCH] Update README.md

---
 README.md | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 30fe1ac..11dbf01 100644
--- a/README.md
+++ b/README.md
@@ -33,12 +33,12 @@ It is a zero configuration fire-and-forget execution agent, providing a full ML/
 
 1. ClearML Server [self-hosted](https://github.com/allegroai/clearml-server)
    or [free tier hosting](https://app.clear.ml)
-2. `pip install clearml-agent` ([install](#installing-the-clearml-agent) the ClearML Agent on any GPU machine:
+2. `pip install clearml-agent` ([install](#installing-the-clearml-agent) the ClearML Agent on any CPU/GPU machine:
    on-premises / cloud / ...)
 3. Create a [job](https://github.com/allegroai/clearml/docs/clearml-task.md) or
    Add [ClearML](https://github.com/allegroai/clearml) to your code with just 2 lines
 4. Change the [parameters](#using-the-clearml-agent) in the UI & schedule for [execution](#using-the-clearml-agent) (or
-   automate with an [AutoML pipeline](#automl-and-orchestration-pipelines-))
+   automate with a [pipelines](#automl-and-orchestration-pipelines-))
 5. :chart_with_downwards_trend: :chart_with_upwards_trend: :eyes:  :beer:
 
 "All the Deep/Machine-Learning DevOps your research needs, and then some... Because ain't nobody got time for that"
@@ -313,21 +313,24 @@ clearml-agent daemon --services-mode --detached --queue services --create-queue
 
 **Note**: It is the user's responsibility to make sure the proper tasks are pushed into the specified queue.
 
-### AutoML and Orchestration Pipelines <a name="automl-pipes"></a>
+### Orchestration and Pipelines <a name="automl-pipes"></a>
 
-The ClearML Agent can also be used to implement AutoML orchestration and Experiment Pipelines in conjunction with the
+The ClearML Agent can also be used to orchestrate and automate Pipelines in conjunction with the
 ClearML package.
 
-Sample AutoML & Orchestration examples can be found in the
-ClearML [example/automation](https://github.com/allegroai/clearml/tree/master/examples/automation) folder.
+Sample automation examples can be found in the
+ClearML [pipelines](https://github.com/allegroai/clearml/tree/master/examples/pipeline) / [automation](https://github.com/allegroai/clearml/tree/master/examples/automation) folder.
 
-AutoML examples
+HPO examples
 
 - [Toy Keras training experiment](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py)
     - In order to create an experiment-template in the system, this code must be executed once manually
-- [Random Search over the above Keras experiment-template](https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py)
+- [Manual Search over the above Keras experiment-template](https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py)
     - This example will create multiple copies of the Keras experiment-template, with different hyper-parameter
       combinations
+- [Optimized Bayesian search over the above Keras experiment-template](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py)
+    - This example will create multiple copies of the Keras experiment-template, with different hyper-parameter combinations launch them on remote machines, monitor the metric (i.e. loss) decide which one has the best potential and abort the others
+
 
 Experiment Pipeline examples