Package: omegaml
omega|ml - MLOps for humanswith just a single line of code you candeploy machine learning models straight from Jupyter Notebook (or any other code)implement data pipelines quickly, without memory limitation, all from a Pandas-like APIserve models and data from an easy to use REST APIFurther, omega|ml is the fastest way toscale model training on the included scalable pure-Python compute cluster, on Spark or any other cloudcollaborate on data science projects easily, sharing Jupyter Notebooksdeploy beautiful dashboards right from your Jupyter Notebook, using dashserveQuick startStart 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 -dJupyter Notebook is immediately available athttp://localhost:8899(omegamlisfunto login).
Any notebook you create will automatically be stored in the integrated omega|ml database, making collaboration a breeze.
The REST API is available athttp://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 omegamlFurther informationDocumentation:https://omegaml.github.io/omegaml/Contributions:http://bit.ly/omegaml-contributeExamples# 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 Casesomega|ml currently supports scikit-learn, Keras and Tensorflow out of the box.
Need to deploy a model from another framework? Open an issue athttps://github.com/omegaml/omegaml/issuesor drop us a line atsupport@omegaml.ioMachine Learning Deploymentdeploy models to production with a single line of codeserve and use models or datasets from a REST APIData Science Collaborationget a fully integrated data science workplace within minuteseasily share models, data, jupyter notebooks and reports with your collaboratorsCentralized Data & Compute clusterperform out-of-core computations on a pure-python or Apache Spark compute clusterhave a shared NoSQL database (MongoDB), out of the box, working like a Pandas dataframeuse a compute cluster to train your models with no additional setupScalability and Extensibilityscale your data science work from your laptop to team to production with no code changesintegrate any machine learning framework or third party data science platform with a common APITowards Data Science recently published an article on omega|ml:https://towardsdatascience.com/omega-ml-deploying-data-machine-learning-pipelines-the-easy-way-a3d281569666In addition omega|ml provides an easy-to-use extensions API to support any kind of models,
compute cluster, database and data source.Commercial Edition & Supporthttps://omegaml.ioomega|ml Commercial 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 athttps://omegaml.io