diff --git a/examples/mnist-keras/README.md b/examples/mnist-keras/README.md
index 795b88f10..c69ad5e3c 100644
--- a/examples/mnist-keras/README.md
+++ b/examples/mnist-keras/README.md
@@ -1,14 +1,15 @@
 # MNIST (TensorFlow/Keras version)
 
-This is a mimimalistic TF/Keras version of the Quickstart Tutorial (PyTorch). For more detailed explaination including a Jupyter Notebook with 
+This is a TF/Keras version of the [Quickstart Tutorial (PyTorch)](https://fedn.readthedocs.io/en/stable/quickstart.html). For more detailed explaination including a Jupyter Notebook with 
 examples of API usage for starting and interacting with federated experiments, refer to that tutorial.
 
 ## Prerequisites
-- [Python 3.8, 3.9 or 3.10](https://www.python.org/downloads)
+- [Python >=3.8, <=3.11](https://www.python.org/downloads)
 - [Docker](https://docs.docker.com/get-docker)
 - [Docker Compose](https://docs.docker.com/compose/install)
 
-## Running the example (pseudo-distributed)
+## Running the example
+
 Clone FEDn and locate into this directory.
 ```sh
 git clone https://github.com/scaleoutsystems/fedn.git
@@ -17,12 +18,38 @@ cd fedn/examples/mnist-keras
 
 ### Preparing the environment, the local data, the compute package and seed model
 
-Start by initializing a virtual enviroment with all of the required dependencies.
+Build a virtual environment (note that you might need to install the 'venv' package):
+
+#### Ubuntu 
+
 ```sh
 bin/init_venv.sh
 ```
 
-Then, to get the data you can run the following script.
+#### MacOS with M1 or M2 processors
+you need another Tensorflow package, as specified in 'requirements-macos.txt' 
+
+```sh
+bin/init_venv_macm1.sh
+```
+
+Activate the virtual environment:
+
+```sh
+source .mnist-keras/bin/activate
+```
+
+Make the compute package (to be uploaded to FEDn):
+```sh
+tar -czvf package.tgz client
+```
+
+Create the seed model (to be uploaded to FEDn):
+```sh
+python client/entrypoint init_seed
+```
+
+Then, to get the data we will use for the clients (MNIST), you can run the following script.
 ```sh
 bin/get_data
 ```
@@ -33,11 +60,20 @@ bin/split_data
 ```
 > **Note**: run with `--n_splits=N` to split in *N* parts.
 
-Create the compute package and a seed model that you will be asked to upload in the next step.
-```
-bin/build.sh
-```
-> The files location will be `package/package.tgz` and `seed.npz`.
+
+Next, you will upload the compute package and seed model to a FEDn network. Here you have two main options: 
+using FEDn Studio (recommended for new users), or a pseudo-local deployment on your own machine.
+
+### If you are using FEDn Studio (recommended):
+
+Follow instructions here to register for Studio and start a project: https://fedn.readthedocs.io/en/stable/studio.html.
+
+In your Studio project: 
+
+- From the "Sessions" menu, upload the compute package and seed model. 
+- Register a client and obtain the corresponding 'client.yaml'.  
+
+
 
 ### Deploy FEDn 
 Now we are ready to deploy FEDn and two clients with `docker-compose`.