diff --git a/samples/prediction/number.csv b/samples/prediction/number.csv
deleted file mode 100644
index 5f63b61..0000000
--- a/samples/prediction/number.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-4, 1
-9, 2
-16, 3
-
diff --git a/samples/prediction/number.pmml b/samples/prediction/number.pmml
deleted file mode 100644
index d81e317..0000000
--- a/samples/prediction/number.pmml
+++ /dev/null
@@ -1,23 +0,0 @@
-<PMML version="4.0" xsi:schemaLocation="http://www.dmg.org/PMML-4_0 http://www.dmg.org/v4-0/pmml-4-0.xsd" xmlns="http://www.dmg.org/PMML-4_0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
-  <Header copyright="Copyright (c) 2011, Google Inc. All rights reserved.">
-    <Application name="Google Prediction API Sample" version="1.4"/>
-  </Header>
-  <DataDictionary numberOfFields="1">
-    <DataField name="X" optype="continuous" dataType="double"/>
-  </DataDictionary>
-  <TransformationDictionary>
-    <DerivedField name="Y1" dataType="double" optype="continuous">
-      <Constant>1.0</Constant>
-    </DerivedField>
-    <DerivedField name="Y2" dataType="double" optype="continuous">
-      <FieldRef field="X"/>
-    </DerivedField>
-    <DerivedField name="Y3" dataType="double" optype="continuous">
-      <Apply function="pow">
-        <FieldRef field="X"/>
-        <Constant>2.0</Constant>
-      </Apply>
-    </DerivedField>
-  </TransformationDictionary>
-</PMML>
-
diff --git a/samples/prediction/prediction.py b/samples/prediction/prediction.py
index 1227019..958d79c 100644
--- a/samples/prediction/prediction.py
+++ b/samples/prediction/prediction.py
@@ -22,7 +22,7 @@
 the setup.sh script to load the sample data to Google Storage.
 
 Usage:
-  $ python prediction.py --object_name="bucket/object"
+  $ python prediction.py --object_name="bucket/object" --id="model_id"
 
 You can also get help on all the command-line flags the program understands
 by running:
@@ -34,53 +34,81 @@
   $ python prediction.py --logging_level=DEBUG
 """
 
-__author__ = 'jcgregorio@google.com (Joe Gregorio)'
+__author__ = ('jcgregorio@google.com (Joe Gregorio), '
+              'marccohen@google.com (Marc Cohen)')
 
 import apiclient.errors
 import gflags
 import httplib2
 import logging
+import os
 import pprint
 import sys
+import time
 
 from apiclient.discovery import build
 from oauth2client.file import Storage
 from oauth2client.client import AccessTokenRefreshError
-from oauth2client.client import OAuth2WebServerFlow
+from oauth2client.client import flow_from_clientsecrets
 from oauth2client.tools import run
 
 FLAGS = gflags.FLAGS
 
-# Set up a Flow object to be used if we need to authenticate. This
-# sample uses OAuth 2.0, and we set up the OAuth2WebServerFlow with
-# the information it needs to authenticate. Note that it is called
-# the Web Server Flow, but it can also handle the flow for native
-# applications <http://code.google.com/apis/accounts/docs/OAuth2.html#IA>
-# The client_id client_secret are copied from the API Access tab on
-# the Google APIs Console <http://code.google.com/apis/console>. When
-# creating credentials for this application be sure to choose an Application
-# type of "Installed application".
-FLOW = OAuth2WebServerFlow(
-    client_id='433807057907.apps.googleusercontent.com',
-    client_secret='jigtZpMApkRxncxikFpR+SFg',
-    scope='https://www.googleapis.com/auth/prediction',
-    user_agent='prediction-cmdline-sample/1.0')
+# CLIENT_SECRETS, name of a file containing the OAuth 2.0 information for this
+# application, including client_id and client_secret, which are found
+# on the API Access tab on the Google APIs
+# Console <http://code.google.com/apis/console>
+CLIENT_SECRETS = 'client_secrets.json'
+
+# Helpful message to display in the browser if the CLIENT_SECRETS file
+# is missing.
+MISSING_CLIENT_SECRETS_MESSAGE = """
+WARNING: Please configure OAuth 2.0
+
+To make this sample run you will need to populate the client_secrets.json file
+found at:
+
+   %s
+
+with information from the APIs Console <https://code.google.com/apis/console>.
+
+""" % os.path.join(os.path.dirname(__file__), CLIENT_SECRETS)
+
+# Set up a Flow object to be used if we need to authenticate.
+FLOW = flow_from_clientsecrets(CLIENT_SECRETS,
+  scope='https://www.googleapis.com/auth/prediction',
+  message=MISSING_CLIENT_SECRETS_MESSAGE)
 
 # The gflags module makes defining command-line options easy for
 # applications. Run this program with the '--help' argument to see
 # all the flags that it understands.
 gflags.DEFINE_enum('logging_level', 'ERROR',
-    ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
-    'Set the level of logging detail.')
+  ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
+  'Set the level of logging detail.')
 
 gflags.DEFINE_string('object_name',
                      None,
                      'Full Google Storage path of csv data (ex bucket/object)')
-
 gflags.MarkFlagAsRequired('object_name')
 
+gflags.DEFINE_string('id',
+                     None,
+                     'Model Id of your choosing to name trained model')
+gflags.MarkFlagAsRequired('id')
+
+# Time to wait (in seconds) between successive checks of training status.
+SLEEP_TIME = 10
+
+def print_header(line):
+  '''Format and print header block sized to length of line'''
+  header_str = '='
+  header_line = header_str * len(line)
+  print '\n' + header_line
+  print line
+  print header_line 
+  
 def main(argv):
-  # Let the gflags module process the command-line arguments
+  # Let the gflags module process the command-line arguments.
   try:
     argv = FLAGS(argv)
   except gflags.FlagsError, e:
@@ -103,40 +131,61 @@
   http = httplib2.Http()
   http = credentials.authorize(http)
 
-  service = build("prediction", "v1.3", http=http)
-
   try:
 
-    # Start training on a data set
-    train = service.training()
-    body = {'id': FLAGS.object_name}
-    start = train.insert(body=body).execute()
+    # Get access to the Prediction API.
+    service = build("prediction", "v1.5", http=http)
+    papi = service.trainedmodels()
 
-    print 'Started training'
+    # List models.
+    print_header('Fetching list of first ten models')
+    result = papi.list(maxResults=10).execute()
+    print 'List results:'
+    pprint.pprint(result)
+
+    # Start training request on a data set.
+    print_header('Submitting model training request')
+    body = {'id': FLAGS.id, 'storageDataLocation': FLAGS.object_name}
+    start = papi.insert(body=body).execute()
+    print 'Training results:'
     pprint.pprint(start)
-
-    import time
-    # Wait for the training to complete
+    
+    # Wait for the training to complete.
+    print_header('Waiting for training to complete')
     while True:
-      try:
-        # We check the training job is completed. If it is not it will return an error code.
-        status = train.get(data=FLAGS.object_name).execute()
-        # Job has completed.
-        pprint.pprint(status)
+      status = papi.get(id=FLAGS.id).execute()
+      state = status['trainingStatus']
+      print 'Training state: ' + state
+      if state == 'DONE':
         break
-      except apiclient.errors.HttpError as error:
-        # Training job not yet completed.
-        print 'Waiting for training to complete.'
-        time.sleep(10)
+      elif state == 'RUNNING':
+        time.sleep(SLEEP_TIME)
+        continue
+      else:
+        raise Exception('Training Error: ' + state)
+          
+      # Job has completed.
+      print 'Training completed:'
+      pprint.pprint(status)
+      break
 
-    print 'Training is complete'
+    # Describe model.
+    print_header('Fetching model description')
+    result = papi.analyze(id=FLAGS.id).execute()
+    print 'Analyze results:'
+    pprint.pprint(result)
 
-    # Now make a prediction using that training
+    # Make a prediction using the newly trained model.
+    print_header('Making a prediction')
     body = {'input': {'csvInstance': ["mucho bueno"]}}
-    prediction = train.predict(body=body, data=FLAGS.object_name).execute()
-    print 'The prediction is:'
-    pprint.pprint(prediction)
+    result = papi.predict(body=body, id=FLAGS.id).execute()
+    print 'Prediction results...'
+    pprint.pprint(result)
 
+    # Delete model.
+    print_header('Deleting model')
+    result = papi.delete(id=FLAGS.id).execute()
+    print 'Model deleted.'
 
   except AccessTokenRefreshError:
     print ("The credentials have been revoked or expired, please re-run"
diff --git a/samples/prediction/prediction_language_id.py b/samples/prediction/prediction_language_id.py
deleted file mode 100644
index 9657b3c..0000000
--- a/samples/prediction/prediction_language_id.py
+++ /dev/null
@@ -1,167 +0,0 @@
-#!/usr/bin/python2.4
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2010 Google Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Simple command-line sample for the Google Prediction API
-
-Command-line application that trains on your input data. This sample does
-the same thing as the Hello Prediction! example. You might want to run
-the setup.sh script to load the sample data to Google Storage.
-
-Usage:
-  $ python prediction_language_id.py --model_id="foo" 
-    --data_file="bucket/object"
-
-You can also get help on all the command-line flags the program understands
-by running:
-
-  $ python prediction_language_id.py --help
-
-To get detailed log output run:
-
-  $ python prediction_language_id.py --logging_level=DEBUG
-"""
-
-__author__ = 'jcgregorio@google.com (Joe Gregorio)'
-
-from apiclient.discovery import build_from_document
-
-import apiclient.errors
-import gflags
-import httplib2
-import logging
-import os
-import pprint
-import sys
-
-from apiclient.discovery import build
-from oauth2client.file import Storage
-from oauth2client.client import AccessTokenRefreshError
-from oauth2client.client import flow_from_clientsecrets
-from oauth2client.tools import run
-
-FLAGS = gflags.FLAGS
-
-# CLIENT_SECRETS, name of a file containing the OAuth 2.0 information for this
-# application, including client_id and client_secret, which are found
-# on the API Access tab on the Google APIs
-# Console <http://code.google.com/apis/console>
-CLIENT_SECRETS = 'client_secrets.json'
-
-# Helpful message to display in the browser if the CLIENT_SECRETS file
-# is missing.
-MISSING_CLIENT_SECRETS_MESSAGE = """
-WARNING: Please configure OAuth 2.0
-
-To make this sample run you will need to populate the client_secrets.json file
-found at:
-
-   %s
-
-with information from the APIs Console <https://code.google.com/apis/console>.
-
-""" % os.path.join(os.path.dirname(__file__), CLIENT_SECRETS)
-
-# Set up a Flow object to be used if we need to authenticate.
-FLOW = flow_from_clientsecrets(CLIENT_SECRETS,
-    scope='https://www.googleapis.com/auth/prediction',
-    message=MISSING_CLIENT_SECRETS_MESSAGE)
-
-# The gflags module makes defining command-line options easy for
-# applications. Run this program with the '--help' argument to see
-# all the flags that it understands.
-gflags.DEFINE_enum('logging_level', 'ERROR',
-    ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
-    'Set the level of logging detail.')
-
-gflags.DEFINE_string('model_id',
-                     None,
-                     'The unique name for the predictive model (ex foo)')
-
-gflags.DEFINE_string('data_file',
-                     None,
-                     'Full Google Storage path of csv data (ex bucket/object)')
-
-gflags.MarkFlagAsRequired('model_id')
-gflags.MarkFlagAsRequired('data_file')
-
-def main(argv):
-  # Let the gflags module process the command-line arguments
-  try:
-    argv = FLAGS(argv)
-  except gflags.FlagsError, e:
-    print '%s\\nUsage: %s ARGS\\n%s' % (e, argv[0], FLAGS)
-    sys.exit(1)
-
-  # Set the logging according to the command-line flag
-  logging.getLogger().setLevel(getattr(logging, FLAGS.logging_level))
-
-  # If the Credentials don't exist or are invalid run through the native client
-  # flow. The Storage object will ensure that if successful the good
-  # Credentials will get written back to a file.
-  storage = Storage('prediction.dat')
-  credentials = storage.get()
-  if credentials is None or credentials.invalid:
-    credentials = run(FLOW, storage)
-
-  # Create an httplib2.Http object to handle our HTTP requests and authorize it
-  # with our good Credentials.
-  http = httplib2.Http()
-  http = credentials.authorize(http)
-
-  service = build("prediction", "v1.4", http=http)
-
-  try:
-
-    # Start training on a data set
-    train = service.trainedmodels()
-    body = {'id': FLAGS.model_id, 'storageDataLocation': FLAGS.data_file}
-    start = train.insert(body=body).execute()
-
-    print 'Started training'
-    pprint.pprint(start)
-
-    import time
-    # Wait for the training to complete
-    while True:
-      try:
-        # We check the training job is completed. If it is not it will return
-        # an error code.
-        status = train.get(id=FLAGS.model_id).execute()
-        # Job has completed.
-        pprint.pprint(status)
-        break
-      except apiclient.errors.HttpError as error:
-        # Training job not yet completed.
-        print 'Waiting for training to complete.'
-        time.sleep(10)
-
-    print 'Training is complete'
-
-    # Now make a prediction using that training
-    body = {'input': {'csvInstance': ["mucho bueno"]}}
-    prediction = train.predict(body=body, id=FLAGS.model_id).execute()
-    print 'The prediction is:'
-    pprint.pprint(prediction)
-
-
-  except AccessTokenRefreshError:
-    print ("The credentials have been revoked or expired, please re-run"
-      "the application to re-authorize")
-
-if __name__ == '__main__':
-  main(sys.argv)
-
diff --git a/samples/prediction/prediction_number.py b/samples/prediction/prediction_number.py
deleted file mode 100644
index 536d6c8..0000000
--- a/samples/prediction/prediction_number.py
+++ /dev/null
@@ -1,175 +0,0 @@
-#!/usr/bin/python2.4
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2010 Google Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Simple command-line sample for the Google Prediction API
-
-Command-line application that trains on your input data. This sample does
-the same thing as the Hello Prediction! example. You might want to run
-the setup.sh script to load both the sample data and the pmml file to 
-Google Storage.
-
-Usage:
-  $ python prediction_number.py --model_id="foo" 
-    --data_file="data_bucket/data_object" --pmml_file="pmml_bucket/pmml_object"
-
-You can also get help on all the command-line flags the program understands
-by running:
-
-  $ python prediction_number.py --help
-
-To get detailed log output run:
-
-  $ python prediction_number.py --logging_level=DEBUG
-"""
-
-__author__ = 'jcgregorio@google.com (Joe Gregorio)'
-
-from apiclient.discovery import build_from_document
-
-import apiclient.errors
-import gflags
-import httplib2
-import logging
-import os
-import pprint
-import sys
-
-from apiclient.discovery import build
-from oauth2client.file import Storage
-from oauth2client.client import AccessTokenRefreshError
-from oauth2client.client import flow_from_clientsecrets
-from oauth2client.tools import run
-
-FLAGS = gflags.FLAGS
-
-# CLIENT_SECRETS, name of a file containing the OAuth 2.0 information for this
-# application, including client_id and client_secret, which are found
-# on the API Access tab on the Google APIs
-# Console <http://code.google.com/apis/console>
-CLIENT_SECRETS = 'client_secrets.json'
-
-# Helpful message to display in the browser if the CLIENT_SECRETS file
-# is missing.
-MISSING_CLIENT_SECRETS_MESSAGE = """
-WARNING: Please configure OAuth 2.0
-
-To make this sample run you will need to populate the client_secrets.json file
-found at:
-
-   %s
-
-with information from the APIs Console <https://code.google.com/apis/console>.
-
-""" % os.path.join(os.path.dirname(__file__), CLIENT_SECRETS)
-
-# Set up a Flow object to be used if we need to authenticate.
-FLOW = flow_from_clientsecrets(CLIENT_SECRETS,
-    scope='https://www.googleapis.com/auth/prediction',
-    message=MISSING_CLIENT_SECRETS_MESSAGE)
-
-# The gflags module makes defining command-line options easy for
-# applications. Run this program with the '--help' argument to see
-# all the flags that it understands.
-gflags.DEFINE_enum('logging_level', 'ERROR',
-    ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
-    'Set the level of logging detail.')
-
-gflags.DEFINE_string('model_id',
-                     None,
-                     'The unique name for the predictive model (ex foo)')
-
-gflags.DEFINE_string('data_file',
-                     None,
-                     'Full Google Storage path of csv data (ex bucket/object)')
-
-gflags.DEFINE_string('pmml_file',
-                     None,
-                     'Full Google Storage path of pmml for '
-                         'preprocessing (ex bucket/object)')
-
-gflags.MarkFlagAsRequired('model_id')
-gflags.MarkFlagAsRequired('data_file')
-gflags.MarkFlagAsRequired('pmml_file')
-
-def main(argv):
-  # Let the gflags module process the command-line arguments
-  try:
-    argv = FLAGS(argv)
-  except gflags.FlagsError, e:
-    print '%s\\nUsage: %s ARGS\\n%s' % (e, argv[0], FLAGS)
-    sys.exit(1)
-
-  # Set the logging according to the command-line flag
-  logging.getLogger().setLevel(getattr(logging, FLAGS.logging_level))
-
-  # If the Credentials don't exist or are invalid run through the native client
-  # flow. The Storage object will ensure that if successful the good
-  # Credentials will get written back to a file.
-  storage = Storage('prediction.dat')
-  credentials = storage.get()
-  if credentials is None or credentials.invalid:
-    credentials = run(FLOW, storage)
-
-  # Create an httplib2.Http object to handle our HTTP requests and authorize it
-  # with our good Credentials.
-  http = httplib2.Http()
-  http = credentials.authorize(http)
-
-  service = build("prediction", "v1.4", http=http)
-
-  try:
-
-    # Start training on a data set
-    train = service.trainedmodels()
-    body = {'id': FLAGS.model_id, 'storageDataLocation': FLAGS.data_file,
-        'storagePMMLLocation': FLAGS.pmml_file}
-    start = train.insert(body=body).execute()
-
-    print 'Started training'
-    pprint.pprint(start)
-
-    import time
-    # Wait for the training to complete
-    while True:
-      try:
-        # We check the training job is completed. If it is not it will return
-        # an error code.
-        status = train.get(id=FLAGS.model_id).execute()
-        # Job has completed.
-        pprint.pprint(status)
-        break
-      except apiclient.errors.HttpError as error:
-        # Training job not yet completed.
-        print 'Waiting for training to complete.'
-        time.sleep(10)
-
-    print 'Training is complete'
-
-    # Now make a prediction using that training
-    body = {'input': {'csvInstance': [ 5 ]}}
-    prediction = train.predict(body=body, id=FLAGS.model_id).execute()
-    print 'The prediction is:'
-    pprint.pprint(prediction)
-
-
-  except AccessTokenRefreshError:
-    print ("The credentials have been revoked or expired, please re-run"
-      "the application to re-authorize")
-
-if __name__ == '__main__':
-  main(sys.argv)
-