--- a/doc/v2_planning/requirements.txt	Mon Sep 13 09:15:25 2010 -0400
+++ b/doc/v2_planning/requirements.txt	Mon Sep 13 09:38:26 2010 -0400
@@ -42,7 +42,7 @@
 R1. reproduce previous work (our own and others')
 
 R2. explore MLA variants by swapping components (e.g.  optimization algo, dataset,
-  hyper-parameters).
+  hyper-parameters)
 
 R3. analyze experimental results (e.g. plotting training curves, finding best
   models, marginalizing across hyper-parameter choices)
@@ -58,7 +58,7 @@
 R7. provide implementations of standard pre-processing algorithms (e.g. PCA,
   stemming, Mel-scale spectrograms, GIST features, etc.)
 
-R8. provide high performance suitable for large-scale experiments,
+R8. provide high performance suitable for large-scale experiments
 
 R9. be able to use the most efficient algorithms in special case combinations of
   learning algorithm components (e.g. when there is a fast k-fold validation
@@ -66,13 +66,17 @@
   to rewrite their standard k-fold validation script to use it)
 
 R10. support experiments on a variety of datasets (e.g. movies, images, text,
-    sound, reinforcement learning?)
+  sound, reinforcement learning?)
 
 R11. support efficient computations on datasets larger than RAM and GPU memory
 
 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
 =====================