omegaml 0.10.2b11

the fastest way to deploy machine learning models

omega|ml is the fastest way to deploy machine learning models

omega|ml takes just a single line of code to

• deploy & embed machine learning models from a easy-to-use REST API

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

• enable scalable model training using the integrated pure-Python compute cluster

In addition you can

• collaborate on data science projects easily (using Jupyter Notebook)

• deploy beautiful dashboards right from your Jupyter Notebook (using dashserve)

Documentation: https://omegaml.github.io/omegaml/

Get started in < 5 minutes

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

$ wget https://raw.githubusercontent.com/omegaml/omegaml/master/docker-compose.yml
$ 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, 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

Examples

Get more information at https://omegaml.github.io/omegaml/

# 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').predict('stats')
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.get('/v1/dataset/stats?sales__gte=100')
requests.put('/v1/model/forecast', json={...})

Use Cases

omega|ml currently supports scikit-learn out of the box. Need to deploy a model from another framework? Open an issue at https://github.com/omegaml/omegaml/issues or drop us a line at [e-mail]

Machine Learning Deployment

• deploy models to production with a single line of code

• serve and use models or datasets from a REST API

Data Science Collaboration

• get a fully integrated data science workplace within minutes [1]

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

Centralized Data & Compute cluster

• perform out-of-core computations on a pure-python or Apache Spark compute cluster [2]

• have a shared NoSQL database, out of the box, that behaves like a Pandas dataframe [3]

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

Scalability and Extensibility

• 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: https://towardsdatascience.com/omega-ml-deploying-data-machine-learning-pipelines-the-easy-way-a3d281569666

[1] supporting scikit-learn, Spark MLLib out of the box, Keras and Tensorflow available shortly. Note the Spark integration is currently only available with the enterprise edition. [2] using Celery, Dask Distributed or Spark [3] leveraging MongoDB’s excellent aggregation framework

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

https://omegaml.io

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 https://omegaml.io