diff --git a/tools/ml/spam.py b/tools/ml/spam.py
new file mode 100755
index 0000000..afc9d4d
--- /dev/null
+++ b/tools/ml/spam.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python
+# 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
+
+"""
+Spam classifier command line tools.
+
+Use this command to submit predictions locally or to the model running
+in production. See tools/spam/README.md for more context on training
+and model operations.
+
+Note that in order for this command to work, you must be logged into
+gcloud in the project under which you wish to run commands.
+"""
+from __future__ import print_function
+from __future__ import division
+from __future__ import absolute_import
+
+import argparse
+import json
+import os
+import re
+import sys
+import googleapiclient
+
+from google.cloud.storage import client, bucket, blob
+import ml_helpers
+from apiclient.discovery import build
+from oauth2client.client import GoogleCredentials
+
+credentials = GoogleCredentials.get_application_default()
+
+# This must be identical with settings.spam_feature_hashes.
+SPAM_FEATURE_HASHES = 500
+
+MODEL_NAME = 'spam_only_words'
+
+
+def Predict(args):
+  ml = googleapiclient.discovery.build('ml', 'v1', credentials=credentials)
+
+  with open(args.summary) as f:
+    summary = f.read()
+  with open(args.content) as f:
+    content = f.read()
+
+  instance = ml_helpers.GenerateFeaturesRaw([summary, content],
+    SPAM_FEATURE_HASHES)
+
+  project_ID = 'projects/%s' % args.project
+  full_model_name = '%s/models/%s' % (project_ID, MODEL_NAME)
+  request = ml.projects().predict(name=full_model_name, body={
+    'instances': [{'inputs': instance['word_hashes']}]
+  })
+
+  try:
+    response = request.execute()
+    print(response)
+  except googleapiclient.errors.HttpError, err:
+    print('There was an error. Check the details:')
+    print(err._get_reason())
+
+
+def LocalPredict(_):
+  print('This will write /tmp/instances.json.')
+  print('Then you can call:')
+  print(('gcloud ml-engine local predict --json-instances /tmp/instances.json'
+    ' --model-dir {model_dir}'))
+
+  summary = raw_input('Summary: ')
+  description = raw_input('Description: ')
+  instance = ml_helpers.GenerateFeaturesRaw([summary, description],
+    SPAM_FEATURE_HASHES)
+
+  with open('/tmp/instances.json', 'w') as f:
+    json.dump({'inputs': instance['word_hashes']}, f)
+
+
+def get_auc(model_name, bucket_obj):
+  bucket_obj.blob = blob.Blob('%s/eval_data.json' % model_name, bucket_obj)
+  data = bucket_obj.blob.download_as_string()
+  data_dict = json.loads(data)
+  return data_dict['auc'], data_dict['auc_precision_recall']
+
+
+def CompareAccuracy(args):
+  client_obj = client.Client(project=args.project)
+  bucket_name = '%s-mlengine' % args.project
+  bucket_obj = bucket.Bucket(client_obj, bucket_name)
+
+  model1_auc, model1_auc_pr = get_auc(args.model1, bucket_obj)
+  print('%s:\nAUC: %f\tAUC Precision/Recall: %f\n'
+        % (args.model1, model1_auc, model1_auc_pr))
+
+  model2_auc, model2_auc_pr = get_auc(args.model2, bucket_obj)
+  print('%s:\nAUC: %f\tAUC Precision/Recall: %f'
+        % (args.model2, model2_auc, model2_auc_pr))
+
+
+def main():
+  if not credentials and 'GOOGLE_APPLICATION_CREDENTIALS' not in os.environ:
+    print(('GOOGLE_APPLICATION_CREDENTIALS environment variable is not set. '
+          'Exiting.'))
+    sys.exit(1)
+
+  parser = argparse.ArgumentParser(description='Spam classifier utilities.')
+  parser.add_argument('--project', '-p', default='monorail-staging')
+
+  project = parser.parse_known_args()
+  subparsers = parser.add_subparsers(dest='command')
+
+  predict = subparsers.add_parser('predict',
+    help='Submit a prediction to the default model in ML Engine.')
+  predict.add_argument('--summary', help='A file containing the summary.')
+  predict.add_argument('--content', help='A file containing the content.')
+
+  subparsers.add_parser('local-predict',
+    help='Create an instance on the local filesystem to use in prediction.')
+
+  ml = googleapiclient.discovery.build('ml', 'v1', credentials=credentials)
+
+  request = ml.projects().models().get(name='projects/%s/models/%s'
+                                       % (project[0].project, MODEL_NAME))
+  response = request.execute()
+
+  default_version = re.search(
+      '.*(spam_trainer_\d+).*',
+      response['defaultVersion']['deploymentUri']).group(1)
+
+  compare = subparsers.add_parser('compare-accuracy',
+                                  help='Compare the accuracy of two models.')
+
+  compare.add_argument('--model1',
+                       default=default_version,
+                       help='The first model to find the auc values of.')
+
+  # TODO(carapew): Make second default the most recently deployed model
+  compare.add_argument('--model2',
+                       default='spam_trainer_1513384515'
+                       if project[0].project == 'monorail-staging' else
+                       'spam_trainer_1522141200',
+                       help='The second model to find the auc values of.')
+
+  args = parser.parse_args()
+
+  cmds = {
+    'predict':  Predict,
+    'local-predict':  LocalPredict,
+    'compare-accuracy': CompareAccuracy,
+  }
+  res = cmds[args.command](args)
+
+  print(json.dumps(res, indent=2))
+
+
+if __name__ == '__main__':
+  main()