Metadata-Version: 2.1
Name: omegaml
Version: 0.13.4
Summary: An open source DataOps, MLOps platform for humans
Home-page: https://omegaml.io/
Author: Patrick Senti
Author-email: patrick.senti@omegaml.io
License: Apache 2.0
Description: omega|ml - 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 the fastest way to
        
        - scale model training on the included scalable pure-Python compute cluster, on Spark or any other cloud
        - collaborate on data science projects easily, sharing Jupyter Notebooks
        - deploy beautiful dashboards right from your Jupyter Notebook, using dashserve
        
        Links
        =====
        
        * Documentation: https://omegaml.github.io/omegaml/
        * Contributions: http://bit.ly/omegaml-contribute
        
        Get started in < 5 minutes
        ==========================
        
        Start the omega|ml server right from your laptop or virtual machine
        
        .. code::
        
            $ 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:
        
        .. code::
        
            # assuming you have started the server as per above
            $ pip install omega|ml
        
        
        Examples
        ========
        
        Get more information at https://omegaml.github.io/omegaml/
        
        .. code::
        
            # 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, Keras and Tensorflow 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 support@omegaml.io
        
        
        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
        - 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
        - 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
        
        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
        
        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
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Operating System :: POSIX :: Linux
Classifier: License :: OSI Approved :: Apache Software License
Description-Content-Type: text/x-rst
Provides-Extra: graph
Provides-Extra: streaming
Provides-Extra: all
Provides-Extra: snowflake
Provides-Extra: sql
Provides-Extra: hdf
Provides-Extra: iotools
Provides-Extra: all-client
Provides-Extra: keras
Provides-Extra: tensorflow
Provides-Extra: dashserve
