Merge pull request #20 from DevoInc/feature/doc-changes

Doc changes

Author: epulidonieves

README.md

@@ -3,15 +3,21 @@
 ![Tests](https://github.com/DevoInc/python-mlmodelmanager-client/actions/workflows/test-tox.yml/badge.svg)
 ![Python](https://img.shields.io/pypi/pyversions/devo-mlmodelmanager) 
 
-# python-mlmodelmanager
-
-An easy-to-use client for Devos’s Machine Learning Model Manager.
-
-Built on top of the widely used Requests library, it takes advantage of all the 
-features it provides and exposes a simplified interface for manage models.
-
-The main purpose of this library is to allow you to focus in the machine learning 
-workflows and not worry about the integration with Devo’s ML Model Manager.
+# Devo Python ML Model Manager Client
+
+The **ML Model Manager** is a service to register machine learning models on 
+[Devo](https://www.devo.com) platform. These models can be used through the 
+query engine using the `mlevalmodel(...)`  operation or through the 
+[FLOW](https://docs.devo.com/space/latest/95213164/Flow) correlation engine 
+including in the context the 
+[MlSingleModelEval](https://docs.devo.com/space/latest/95214962/ML+Single+Model+Evaluator)
+unit.
+
+**devo-mlmodelmanager** provides an easy-to-use client for Devo’s ML Model
+Manager. Built on top of the widely used 
+[Requests](https://requests.readthedocs.io/en/latest/) library exposes a 
+simplified interface for model management, allowing you to focus in the machine
+learning workflows and not worry about the integration with Devo platform.
 
 ## A simple example
 

docs/source/conf.py

@@ -9,7 +9,7 @@
 import importlib.metadata
 
 
-project = 'ML Model Manager'
+project = 'ML Model Manager Client'
 copyright = '2022, Devo ML Team'
 author = 'Devo ML Team'
 version = importlib.metadata.version("devo-mlmodelmanager")

docs/source/index.rst

@@ -1,14 +1,19 @@
-Devo ML Model Manager
-=====================
-
-An easy-to-use client for Devos's Machine Learning Model Manager server.
-
-Built on top of the widely used :doc:`Requests <requests:index>` library, it
-takes advantage of all the features it provides and exposes a simplified
-interface for manage models.
-
-The main purpose of this library is to allow you focus in the machine learning
-workflows and not worry about the integration with Devo's ML Model Manager.
+Devo ML Model Manager Client
+============================
+
+The **ML Model Manager** is a service to register machine learning models on
+`Devo <https://www.devo.com>`_ platform. These models can be used through the
+query engine using the ``mlevalmodel(...)`` operation or through the
+`FLOW <https://docs.devo.com/space/latest/95213164/Flow>`_ correlation engine
+including in the context the
+`MlSingleModelEval <https://docs.devo.com/space/latest/95214962/ML+Single+Model+Evaluator>`_
+unit.
+
+devo-mlmodelmanager provides an easy-to-use client for Devo’s ML Model
+Manager. Built on top of the widely used :doc:`Requests <requests:index>`
+library exposes a simplified interface for model management, allowing you to
+focus on the machine learning workflows and not worry about the integration
+with Devo platform.
 
 A quick example
 ---------------

docs/source/user_guide/client-object.rst

@@ -38,7 +38,7 @@ In this example we don't tell the client to use any
 :ref:`downloader <Downloaders>` via the constructor keyword `downloader`. In
 that case a
 :class:`FileSystemDownloader <devo_ml.modelmanager.downloader.FileSystemDownloader>`
-will be used as a fallback downloader with the current directory as the path.
+will be used as a fallback downloader with the current directory as path.
 Neither you don't need to worry about downloaders right now because most of the
 time the default downloader is fine, but you could create a client with your
 customize downloader.

docs/source/user_guide/downloaders.rst

@@ -87,6 +87,7 @@ the model files. Let's implement a AWS S3 Bucket downloader.
                 self.bucket,
                 f"{name}{ext}"
             )
+            return f"{self.bucket}/{name}{ext}"
 
 .. warning::
 

docs/source/user_guide/functions-facade.rst

@@ -34,7 +34,7 @@ If the model you are trying to add already exists in the system, a
 :exc:`ModelAlreadyExists <devo_ml.modelmanager.error.ModelAlreadyExists>`
 error will be thrown.
 
-Trough the `force` parameter you can mange whether to override the model.
+Trough the `force` parameter you can manage whether to override the model.
 
 
 :func:`get_model(...) <devo_ml.modelmanager.get_model>`