diff --git a/features/spammodel.py b/features/spammodel.py
new file mode 100644
index 0000000..dc5e715
--- /dev/null
+++ b/features/spammodel.py
@@ -0,0 +1,92 @@
+# Copyright 2016 The Chromium Authors. All rights reserved.
+# Use of this source code is governed by a BSD-style
+# license that can be found in the LICENSE file or at
+# https://developers.google.com/open-source/licenses/bsd
+""" Tasks and handlers for maintaining the spam classifier model. These
+    should be run via cron and task queue rather than manually.
+"""
+from __future__ import print_function
+from __future__ import division
+from __future__ import absolute_import
+
+import csv
+import logging
+import webapp2
+import cloudstorage
+import json
+
+from datetime import date
+from datetime import datetime
+from datetime import timedelta
+from google.appengine.api import app_identity
+
+from framework import cloud_tasks_helpers
+from framework import gcs_helpers
+from framework import servlet
+from framework import urls
+
+class TrainingDataExport(webapp2.RequestHandler):
+  """Trigger a training data export task"""
+  def get(self):
+    task = cloud_tasks_helpers.generate_simple_task(
+        urls.SPAM_DATA_EXPORT_TASK + '.do', {})
+    cloud_tasks_helpers.create_task(task)
+
+
+BATCH_SIZE = 1000
+
+class TrainingDataExportTask(servlet.Servlet):
+  """Export any human-labeled ham or spam from the previous day. These
+     records will be used by a subsequent task to create an updated model.
+  """
+  CHECK_SECURITY_TOKEN = False
+
+  def ProcessFormData(self, mr, post_data):
+    logging.info("Training data export initiated.")
+
+    bucket_name = app_identity.get_default_gcs_bucket_name()
+    date_str = date.today().isoformat()
+    export_target_path = '/' + bucket_name + '/spam_training_data/' + date_str
+    total_issues = 0
+
+    with cloudstorage.open(export_target_path, mode='w',
+        content_type=None, options=None, retry_params=None) as gcs_file:
+
+      csv_writer = csv.writer(gcs_file, delimiter=',', quotechar='"',
+          quoting=csv.QUOTE_ALL, lineterminator='\n')
+
+      since = datetime.now() - timedelta(days=7)
+
+      # TODO: Further pagination.
+      issues, first_comments, _count = (
+          self.services.spam.GetTrainingIssues(
+              mr.cnxn, self.services.issue, since, offset=0, limit=BATCH_SIZE))
+      total_issues += len(issues)
+      for issue in issues:
+        # Cloud Prediction API doesn't allow newlines in the training data.
+        fixed_summary = issue.summary.replace('\r\n', ' ')
+        fixed_comment = first_comments[issue.issue_id].replace('\r\n', ' ')
+        email = self.services.user.LookupUserEmail(mr.cnxn, issue.reporter_id)
+        csv_writer.writerow([
+            'spam' if issue.is_spam else 'ham',
+            fixed_summary.encode('utf-8'), fixed_comment.encode('utf-8'), email,
+        ])
+
+      comments = (
+          self.services.spam.GetTrainingComments(
+              mr.cnxn, self.services.issue, since, offset=0, limit=BATCH_SIZE))
+      total_comments = len(comments)
+      for comment in comments:
+        # Cloud Prediction API doesn't allow newlines in the training data.
+        fixed_comment = comment.content.replace('\r\n', ' ')
+        email = self.services.user.LookupUserEmail(mr.cnxn, comment.user_id)
+        csv_writer.writerow([
+            'spam' if comment.is_spam else 'ham',
+            # Comments don't have summaries, so it's blank:
+            '', fixed_comment.encode('utf-8'), email
+        ])
+
+    self.response.body = json.dumps({
+        "exported_issue_count": total_issues,
+        "exported_comment_count": total_comments,
+    })