Python AI/ML analytics collaboration & deployment for humans
Python analytics
in the cloud
made easy
Collaborate, Deploy, Deliver.
How it Works
Shared notebooks.
Larger than memory datasets.
Instant deployment.
Build your data pipelines and models with the Python tools you already know and love. omega|ml makes working with the cloud fully Pythonic:
01.
Publish models & datasets from code or using our cli
om.models.put(model, ‘sales-prediction’)
om.datasets.put(dataframe, ‘sales-data’)
02.
Use the cloud for data processing & model training
om.runtime.script('mypipeline').run()
om.runtime.model('sales-prediction').fit(X, Y)
03.
Leverage the instant REST API from any application:
HTTP GET /api/v1/model/sales-prediction/predict
HTTP PUT /api/v1/dataset/sales-data/
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
Scalable NoSQL database for any-size data sets
Instant REST API for data, models, reports
Secure Jupyter Notebooks ready for collaboration
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
COMPUTE & DATAFRAME
Add processing options such as AutoML or Model Visualization