Start here

import omegaml as om

The om module is readily configured to work with your local omega|ml server, or with the cloud instance configured using the om cloud login command.

Once loaded om provides several storage areas that are immediately usable:

  • om.datasets - storage area for Python and Pandas objects

  • om.models - storage area for models

  • om.scripts - storage area for custom modules (a.k.a. lambda modules)

  • storage area for jobs (ipython notebooks)

In addition, your cluster or cloud resources are available as

  • om.runtime - the omega|ml remote execution environment

Run in the cloud

Run locally

Start the omega|ml server right from your laptop or virtual machine

$ wget
$ docker-compose up -d

Jupyter Notebook is immediately available at http://localhost:8899 (omegamlisfun to login). Any notebook you create will automatically be stored in the integrated omega|ml database (backed by MongoDB), making collaboration a breeze. The REST API is available at http://localhost:5000.

Already have a Python environment (e.g. Jupyter Notebook)? Leverage the power of omega|ml by installing as follows:

# assuming you have started the server as per above
$ pip install omegaml

DataOps & MLOps for humans

with just a single line of code you can

  • deploy machine learning models straight from Jupyter Notebook (or any other code)

  • implement data pipelines quickly, without memory limitation, all from a Pandas-like API

  • serve models and data from an easy to use REST API

Further, omega|ml is one of the fastest, most straight forward ways to

  • leverage cloud resources to scale model training in a dynamic compute cluster

  • collaborate on data science projects easily, sharing Jupyter Notebooks, datasets, models, scripts

  • deploy dashboards and applications right from your Jupyter Notebook


Get more information at

# transparently store Pandas Series and DataFrames or any Python object
om.datasets.put(df, 'stats')
om.datasets.get('stats', sales__gte=100)

# transparently store and get models
clf = LogisticRegression()
om.models.put(clf, 'forecast')
clf = om.models.get('forecast')

# run and scale models directly on the integrated Python or Spark compute cluster
om.runtime.model('forecast').fit('stats[^sales]', 'stats[sales]')
om.runtime.model('forecast').gridsearch(X, Y)

# use the REST API to store and retrieve data, run predictions
requests.put('/v1/dataset/stats', json={...})
requests.put('/v1/model/forecast', json={...})

Use Cases

omega|ml currently supports scikit-learn, Keras and Tensorflow out of the box. Need to deploy a model from another framework? Open an issue at or drop us a line at

  • deploy models to production with a single line of code

  • serve and use models or datasets from a REST API

  • get a fully integrated data science workplace within minutes

  • easily share models, data, jupyter notebooks and reports with your collaborators

  • perform out-of-core computations on a pure-python or Apache Spark compute cluster

  • have a shared NoSQL database (MongoDB), out of the box, working like a Pandas dataframe

  • use a compute cluster to train your models with no additional setup

  • scale your data science work from your laptop to team to production with no code changes

  • integrate any machine learning framework or third party data science platform with a common API

Towards Data Science recently published an article on omega|ml:

In addition omega|ml provides an easy-to-use extensions API to support any kind of models, compute cluster, database and data source.

Enterprise Edition

omega|ml Enterprise Edition provides security on every level and is ready made for Kubernetes deployment. It is licensed separately for on-premise, private or hybrid cloud. Sign up at