--- a/doc/v2_planning/requirements.txt	Mon Sep 13 09:38:49 2010 -0400
+++ b/doc/v2_planning/requirements.txt	Mon Sep 13 09:42:36 2010 -0400
@@ -72,32 +72,8 @@
 
 R12. support infinite datasets (i.e. generated on the fly)
 
-R13. from a given evaluation experimental setup, be able to save a model that
-  can be used "in production" (e.g. say you try many combinations of
-  preprocessing, models and associated hyper-parameters, and want to easily be
-  able to recover the full "processing pipeline" that performs best, to be
-  used on future "real" test data)
-
-Basic Design Approach
-=====================
-
-An ability to drive parallel computations is essential in addressing [R6,R8].
+R13. apply trained models "in production".
+  - e.g. say you try many combinations of preprocessing, models and associated
+    hyper-parameters, and want to easily be able to recover the full "processing
+    pipeline" that performs best, and use it on real/test data later.
 
-The basic design approach for the library is to implement 
-- a few virtual machines (VMs), some of which can run programs that can be
-  parallelized across processors, hosts, and networks.
-- MLAs in a Symbolic Expression language (similar to Theano) as required by
-  [R5,R7,R8]
-
-MLAs are typically specified by Symbolic programs that are compiled to these
-instructions, but some MLAs may be implemented in these instructions directly.
-Symbolic programs are naturally modularized by sub-expressions [R2] and can be
-optimized automatically (like in Theano) to address [R9].
-
-A VM that caches instruction return values serves as 
-- a reliable record of what jobs were run [R1]
-- a database of intermediate results that can be analyzed after the
-  model-training jobs have completed [R3]
-- a clean API to several possible storage and execution backends.
-
-