diff --git a/README.md b/README.md
index 4645a96..09ce27b 100644
--- a/README.md
+++ b/README.md
@@ -15,74 +15,17 @@ The only requirement is docker installed on your system and we are going to use
  - Ready to use bash scripts for python development!
  - Automatically-created s3 buckets
 
-
-## Simple setup guide
+# 🚀 Setup guide 
 1. Configure `.env` file for your choice. You can put there anything you like, it will be used to configure you services
+2. Run `docker compose up`
+3. Open up http://localhost:5000 for MlFlow, and http://localhost:9001/ to browse your files in S3 artifact store
 
-2. Run the Infrastructure by this one line:
-```shell
-$ docker-compose up -d
-Creating network "mlflow-basis_A" with driver "bridge"
-Creating mlflow_db      ... done
-Creating tracker_mlflow ... done
-Creating aws-s3         ... done
-```
+## How to use in ML development in python
 
-3. Create mlflow bucket. You can use my bundled script.
+<details>
+<summary>Click to show</summary>
 
-Just run 
-```shell 
-bash ./run_create_bucket.sh
-```
-
-You can also do it **either using AWS CLI or Python Api**.
-<details><summary>AWS CLI</summary>
-
-1. [Install AWS cli](https://aws.amazon.com/cli/) **Yes, i know that you dont have an Amazon Web Services Subscription - dont worry! It wont be needed!**
-2. Configure AWS CLI - enter the same credentials from the `.env` file
-
-```shell
-aws configure
-```
-> AWS Access Key ID [****************123]: AKIAIOSFODNN7EXAMPLE  
-> AWS Secret Access Key [****************123]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY  
-> Default region name [us-west-2]: us-east-1  
-> Default output format [json]: <ENTER>  
-
-3. Run
-```shell
-aws --endpoint-url=http://localhost:9000 s3 mb s3://mlflow
-```
-
-</details>
-
-<details><summary>Python API</summary>
-
-1. Install Minio
-```shell
-pip install Minio
-```
-2. Run this to create a bucket
-```python
-from minio import Minio
-from minio.error import ResponseError
-
-s3Client = Minio(
-    'localhost:9000',
-    access_key='<YOUR_AWS_ACCESSS_ID>', # copy from .env file
-    secret_key='<YOUR_AWS_SECRET_ACCESS_KEY>', # copy from .env file
-    secure=False
-)
-s3Client.make_bucket('mlflow')
-```
-
-</details>
-
----
-
-4. Open up http://localhost:5000 for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from `.env` file
-
-5. Configure your client-side
+1. Configure your client-side
 
 For running mlflow files you need various environment variables set on the client side. To generate them user the convienience script `./bashrc_install.sh`, which installs it on your system or `./bashrc_generate.sh`, which just displays the config to copy & paste.
 
@@ -91,7 +34,7 @@ For running mlflow files you need various environment variables set on the clien
 
 The script installs this variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, MLFLOW_S3_ENDPOINT_URL, MLFLOW_TRACKING_URI. All of them are needed to use mlflow from the client-side.
 
-6. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda`
+2. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda`
 
 ```shell
 mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
@@ -99,12 +42,15 @@ mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
 python ./quickstart/mlflow_tracking.py
 ```
 
-7. *(Optional)* If you are constantly switching your environment you can use this environment variable syntax
+3. *(Optional)* If you are constantly switching your environment you can use this environment variable syntax
 
 ```shell
 MLFLOW_S3_ENDPOINT_URL=http://localhost:9000 MLFLOW_TRACKING_URI=http://localhost:5000 mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
 ```
+
+</details>
  
+
 ## Licensing
 Copyright (c) 2021 Tomasz Dłuski