diff --git a/README.md b/README.md
index f679562..bd2ea1a 100644
--- a/README.md
+++ b/README.md
@@ -2,14 +2,19 @@
 
 If you want to boot up mlflow project with one-liner - this repo is for you. 
 
-The only requirement is docker installed on your system
+The only requirement is docker installed on your system and we are going to use Bash on linux/windows.
 
 AWS S3 based [on this article ](https://dev.to/goodidea/how-to-fake-aws-locally-with-localstack-27me)
 
 
+1. Configure `.env` file for your choice
+
+2. Create mlflow bucket. You can do it either using AWS CLI or Python Api
+
+<summary>AWS CLI
+<details>
 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 `.env` file for your choice
-3. Configure AWS CLI - enter the same credentials from the `.env` file
+2. Configure AWS CLI - enter the same credentials from the `.env` file
 
 ```shell
 aws configure
@@ -19,21 +24,51 @@ aws configure
 > Default region name [us-west-2]: us-east-1  
 > Default output format [json]: <ENTER>  
 
-4. Create mlflow bucket
-
+3. Run
 ```shell
 aws --endpoint-url=http://localhost:9000 s3 mb s3://mlflow
 ```
+</details>
+</summary>
 
-5. Open up http://localhost:5000/#/ for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from `.env` file
+<summary>Python API
+<details>
 
-6. Configure S3 Keys.
+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='AKIAIOSFODNN7EXAMPLE', # copy from .env file
+    secret_key='wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY', # copy from .env file
+    secure=False
+)
+s3Client.make_bucket('mlflow')
+```
+
+</details>
+</summary>
+
+
+3. Open up http://localhost:5000/#/ for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from `.env` file
+
+4. Configure your client-side
 
 For running mlflow files you AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables present on the client-side.
 
+Also, you will need to specify the address of your S3 server (minio) and mlflow tracking server
+
 ```shell
 export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
 export AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+export MLFLOW_S3_ENDPOINT_URL=http://localhost:9000
+export MLFLOW_TRACKING_URI=http://localhost:5000
 ```
 
 You can load them from the .env file like so
@@ -51,18 +86,16 @@ source ~/.bashrc
 7. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda`
 
 ```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
+mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
 ```
 
 Optionally you can run 
 ```shell
-MLFLOW_S3_ENDPOINT_URL=http://localhost:9000 MLFLOW_TRACKING_URI=http://localhost:5000 python ./quickstart/mlflow_tracking.py
-
+python ./quickstart/mlflow_tracking.py
 ```
 
-8. To make the setting permament move the MLFLOW_S3_ENDPOINT_URL and MLFLOW_TRACKING_URI into your .bashrc
+8. (Optional) If you are constantly switching your environment you can use this environment variable syntax
 
-```bash
-export MLFLOW_S3_ENDPOINT_URL=http://localhost:9000
-export MLFLOW_TRACKING_URI=http://localhost:5000
+```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
 ```