Deploy your AI/ML analytics pipeline in seconds, not months.

 

How it Works

Instant Deployment

Build your models with the tools you love. Then publish the complete model & data pipeline straight from your code.

01.

Publish the model & data for training and validation
om.models.put(model_pipeline, ‘sales-prediction’)
om.datasets.put(dataframe, ‘sales-data’)

02.

Leverage the instant REST API from any application:
HTTP GET /api/v1/model/sales-prediction/predict
HTTP PUT /api/v1/dataset/sales-data/

This takes just seconds. Imagine what your whole team can achieve in a month. And there is more!

Any Framework

These frameworks are supported out of the box. Any framework can be supported using plugins.

What is Included

Fully installed, customizable Python data science distribution

Scalable compute cluster for model training & prediction

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Scalable NoSQL database for any-size data sets

Instant REST API for data, models, reports

Secure Jupyter Notebooks ready for collaboration

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Notebook scheduling for controlled batch execution

 

Cloud & Enterprise Add-on Features

  • Jupyter Notebook publishing as reports and presentations

  • Instant Plotly Dashboard deployment

  • Mini batch framework for streaming and IoT data

  • Run on Apache Spark or Anaconda Distributed cluster

  • Deployment integration for any cloud backend

  • Enterprise-grade security

 

Why omega | ml

01.

Flexible

Leverage Python machine learning models & pipelines in any application, straight from our easy-to-use REST API
 

02.

Fast

Collaborate instantly on any data science project, using the tools you love (e.g. Python, scikit-learn, Jupyter Notebook)

03.

Scalable

Scale model training and prediction from any client, applying the power of the built-in compute cluster

04.

Cost-Effective

Large-scale datasets at a fraction of the cost of other solutions (no need to run Apache Hadoop or Spark)​

05.

No Vendor Lock-in

Our fully open source core and support for any Kubernetes cloud means you can deploy anywhere.

06.

Secure & Independent

Our compute center in Switzerland meets all your data privacy and security requirements.

 

From Your Lab to Production

omega|ml is your one-stop hub to build, productize and launch your AI/ML project

Innovate

More and much faster:

Data Scientists continue working with  the Python tools they trust & love.

 

Working right out of Jupyter Notebook or any other IDE, omega|ml does not stand in your way. Yet it is always ready to deploy and collaborate on datasets and models.

 

All it takes is a single line of code.

Collaborate easily:

Collaborate

Ever wondered where to store all those .csv files? How to share your  notebooks? How to persist and deploy your models?

 

Sure there are ways. But they are all complicated.

 

omega|ml provides collaboration out of the box, for datasets, models, pipelines and applications

Productize

Launch your app today:

Want to integrate your datasets and models into an application? Don’t waste weeks or months to build your own. omega|ml is ready in minutes.

 

omega|ml publishes datasets, models and dash apps with a single line of code.  Once published you get a nice, ready-to use REST API and app URLs. Scheduled data pipelines included.

Leverage Swiss cloud power:

Scale

The built-in compute cluster provides instant, no-hassle, scalable model training and prediction. Support for Dask Distributed, Hadoop and Apache Spark is available free of charge.

 

As a private or public IaaS/PaaS provider, deploy omega|ml Enterprise Edition to offer your clients a scalable Data Science and ML Platform As a Service

Extensible Architecture

Storage & Compute Backends, DataFrame Operations

omega|ml comes with batteries included, however new requirements are not a problem: alternate data sources or sinks and data pipelines can easily be added.

The following extension points are available:

STORAGE BACKEND

Extend what  objects can be stored and retrieved

RUNTIME BACKEND

Add any compute backend to run any omega|ml-stored models through the same API

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COMPUTE & DATAFRAME

Add processing options such as AutoML or Model Visualization

Python AI/ML analytics deployment & collaboration for humans

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