Updated readme

README.rst

@@ -2,6 +2,17 @@ mlpipeline
 ==========
 This is a simple frawork to organize you machine learning workflow. It automates most of the basic functionalities such as logging, a framework for testing models and gluing together different steps at different stages. This project came about as a result of me abstracting the boilerplate code and automating different parts of the process.
 
+The aim of this simple framework is to consolidate the different sub-problems (such as loading data, model configurations, training process, evalutaion process, exporting trained models, etc.) when working/researching with machine learning models. This allows the user to define how the different sub-problems are to be solved using their choice of tools and mlpipeline would handle piecing them together.
+
+Core operations
+---------------
+This framework chains the different operations (sub-problems) depending on the mode it is executed in. mlpipeline currently has 3 modes:
+- TEST mode: When in TEST mode, it doesn't perform any logging or tracking. It creates a temporory empty directory for the experiment to store the artifacts of an experiment in. When developing and testing the different operations, this mode can be used.
+- RUN mode: In this mode, logging and tracking is performed. In addition, for each experiment run (refered to as a experiment version in mlpipeline) a directory is created for artifacts to be stored.
+- EXPORT mode: In this mode, the exporting related operations will be executed instead of the training/evaluation related operations.
+
+In addition to providing different modes, the pipeline also supports logging and recording various details. Currently mlpipeline records all logs, metrics and artifacts using a bacis log files as well using `mlflow <https://github.com/databricks/mlflow>`_.
+
 Documentation
 -------------
 The documentation is hosted at `ReadTheDocs <https://mlpipeline.readthedocs.io/>`_.