user roles

source/user/overview/user-roles.rst

@@ -1,2 +1,56 @@
 Our different user roles
 ========================
+
+.. todo:: Check the bullet points for correctness and add additional bullet points
+
+DEEPaaS is focused with three different types of users in mind, whcih vary in their machine learning knowledge and their
+usage of DEEPaaS.
+
+The basic user
+--------------
+
+This user wants to use the tools that have been already developed and *apply them to their data*, and therefore don't need
+to have any machine learning knowledge. For example, they can take an already trained network for plant classification
+that has been containerized, and use it to classify their own plant images.
+
+**What DEEPaaS can offer to you:**
+
+* a catalogue full of ready-to-use modules to make inference with your data
+* solutions to run the inference in the Cloud or in your local resources
+* the ability to develop complex topologies by composing different modules
+
+
+The intermediate user
+---------------------
+
+The intermediate user wants to *retrain the available tools* to perform the same task but fine tuning them to their own data.
+They still might not need high level knowledge on modelling of machine learning problems, but typically do need basic
+programming skills to prepare their own data into the appropriate format.
+Nevertheless, they can re-use the knowledge being captured in a trained network and adjust the network to their problem
+at hand by re-training the network on their own dataset.
+An example could be a user who takes the plant classification tool but retrains it to perform animal classification.
+
+**What DEEPaaS can offer to you:**
+
+* the ability to train out-of-the-box a tool of the catalogue on your personal dataset
+* access to data storage resources via NextCloud to save your dataset
+* the ability to deploy the developed tool on Cloud resources
+* the ability to share the tool with other users in the user's catalogue
+
+
+The advanced user
+-----------------
+
+The advanced users are the ones that will *develop their own machine learning tools* and therefore need to be competent
+in machine learning. This would be the case for example if we provided an image classification tool but the users
+wanted to perform object localisation, which is a fundamentally different task.
+Therefore they will design their own neural network architecture, potentially re-using parts of the code from other
+tools.
+
+**What DEEPaaS can offer to you:**
+
+* a ready-to-use environment with the main DL frameworks running in a dockerized solution running on different types of
+  hardware (CPUs, GPUs, etc)
+* access to data storage resources via NextCloud to save your dataset
+* the ability to deploy the developed tool on Cloud resources
+* the ability to share the tool with other users in the user's catalogue