Introduction to models

omega|ml currently implements the following machine learning frameworks out of the box. More backends are planned. Any backend can be implemented using the backend API.

  • scikit-learn

  • Keras

  • Tensorflow (tf.keras, tf.estimator,, tf.SavedModel)

  • Apache Spark MLLib

Note that support for Keras, Tensorflow and Apache Spark is experimental at this time.

Storing models

Storing models and pipelines is as straight forward as storing Pandas DataFrames and Series. Simply create the model, then use om.models.put() to store:

from sklearn.linear_model import LinearRegression

# train a linear regression model
df = pd.DataFrame(dict(x=range(10), y=range(20,30)))
clf = LinearRegression()[['x']], df[['y']])
# store the trained model
om.models.put(clf, 'lrmodel')

Models can also be stored untrained:

df = pd.DataFrame(dict(x=range(10), y=range(20,30)))
clf = LinearRegression()
# store the trained model
om.models.put(clf, 'lrmodel')

Using models to predict

Retrieving a model is equally straight forward:

clf = om.models.get('lrmodel')
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

Once retrieved the model can be accessed as any model kept in memory, e.g. to predict using new data:

clf = om.models.get('lrmodel')
df = pd.DataFrame(dict(x=range(70,80)))
array([[ 90.],
   [ 91.],
   [ 92.],
   [ 93.],
   [ 94.],
   [ 95.],
   [ 96.],
   [ 97.],
   [ 98.],
   [ 99.]])

Model versioning

By default all models are versioned automatically. A model is a saved instance of the model that is connected to the same name. The following example will store two model versions, the first is not trained and thus cannot be used for prediction, the second is fitted and thus can be used for prediction:

reg = LinearRegression()
om.models.put(reg, 'mymodel'), Y)
om.models.put(reg, 'mymodel')

Model versions can be accessed by specifying the version as part of the name:

# get the latest model, note @latest is implied if not specified

Previous versions can be referenced by specifying ^ for each previous version, or by specifying a tag on storage:

# retrieve one version before latest
# retrieve two versions before latest

# store a new version, give it a name
om.models.put('mymodel', tag='experiment')
# retrieve the @experiment model

To see all revisions of a model use om.models.revisions('mymodel')

[('e05bd064dbc9258df929d4099a02ad5452d73389', ''),
('aef452194c1671e5b8a496bfbbba75d83bb51b91', ''),
('3ca9aef680612bbfa0d2ac67a1b2bdbd73b976f0', ['latest', 'experiment'])]

Note version naming works across all parts of omega|ml, e.g.

# use the runtime to work with a particular model version

# works by the cli, too
$ om runtime model 'mymodel@experiment' fit ...

# works on the API, too
$ curl http://hostname/api/v1/model/mymodel@experiment/fit?datax=...&datay...

Using the compute cluster


omega|ml provides a state-of-the art compute cluster, called the runtime. Using the runtime you can delegate model tasks to the cluster:

model = om.runtime.model('lrmodel')
result = model.predict(df[['x']])
array([[ 20.],
   [ 21.],
   [ 22.],
   [ 23.],
   [ 24.],
   [ 25.],
   [ 26.],
   [ 27.],
   [ 28.],
   [ 29.]])

Note that the result is a deferred object that we resolve using get.

Instead of passing data, you may also pass the name of a DataFrame stored in omegaml:

# create a dataframe and store it
df = pd.DataFrame(dict(x=range(70,80)))
om.datasets.put(df, 'testlrmodel')
# use it to predict
result = om.runtime.model('lrmodel').predict('testlrmodel')

Model Fitting

To train a model using the runtime, use the fit method on the runtime’s model, as you would on a local model:

# create a dataframe and store it
df = pd.DataFrame(dict(x=range(10), y=range(20,30)))
om.datasets.put(df, 'testlrmodel')
# use it to fit the model
result = om.runtime.model('lrmodel').fit('testlrmodel[x]', 'testlrmodel[y]')


currently supported for sckit-learn

To use cross validated grid search on a model, use the gridsearch method on the runtime’s model. This creates, fits and stores a GridSearchCV instance and automatically links it to the model. Use the GridSearchCV model to evaluate the performance of multiple parameter settings.


Instead of using this default implementation of GridSearchCV you may create your own GridSearchCV instance locally and then fit it using the runtime. In this case be sure to link the model used for grid searching and the original model by changing the attributes on the model’s metadata.

X, y = make_classification()
logreg = LogisticRegression()
om.models.put(logreg, 'logreg')
params = {
    'C': [0.1, 0.5, 1.0]
# gridsearch on runtime
om.runtime.model('logreg').gridsearch(X, y, parameters=params)
meta = om.models.metadata('logreg')
# check gridsearch was saved
self.assertIn('gridsearch', meta.attributes)
self.assertEqual(len(meta.attributes['gridsearch']), 1)
self.assertIn('gsModel', meta.attributes['gridsearch'][0])
# check we can get back the gridsearch model
gs_model = om.models.get(meta.attributes['gridsearch'][0]['gsModel'])
self.assertIsInstance(gs_model, GridSearchCV)

Other Model tasks

The runtime provides more than just model training and prediction. The runtime implements a common API to all supported backends that follows the scikit-learn estimator model. That is the runtime supports the following methods on a model:

  • fit

  • partial_fit

  • transform

  • score

  • gridsearch

For details refer to the API reference.