Omega|ML

omega|ml is an innovative Python-native DataOps and MLOps platform that provides a ready-to-use data science workbench, development and runtime environment.

It is unique in its “single line of code” approach and open architecture, providing a fully integrated cloud-native and scalable compute and storage facility for all your analytics needs. Built on well-known open-source technology, omega|ml removes vendor lock-in and saves up to 90% of the time investment compared with custom-engineered solutions.

At the core, omega|ml is provided as open source. Its open architecture uses a unique plug-in system that enables DataOps teams to keep their existing data science libraries such as Pandas, Tensorflow, Keras and PyTorch, while gaining the ability to easily store, process, train and run all data and models.

omega|ml’s integrated hybrid analytics storage, built on MongoDB, provides the Python-native and Pandas-like API familiar to most data scientists. This allows any-size datasets to be processed at ease and without the excessively high memory needs or computation delays of other solutions.

Do you see any of the following common situations in your organisation? omega|ml helps your team to contribute real business value, today:

• Data scientists struggle to collaborate on notebooks, datasets and models. Lots of expensive custom code gets written, much time is spent just to move data around, using deployment tools that are well outside the core skill set of the data science team.
• Operationalising analytics solutions takes forever to complete. Often, software engineers complain that the data scientists’ code is not ready for production and that they must start from scratch, possibly using a “proper language” or taking a fresh approach.
• Integrating analytics and development tools is cumbersome, time-ineffective and too technology driven. Businesses need to leverage AI’s value today, there is no time to waste by getting your team up to speed on the most recent all-fancy technology set (examples include in-memory, Spark, Hadoop, Scala, Kubernetes, and many more).
• In-house custom-developed platforms have outgrown their purpose. Building readily available infrastructure is costly, time-consuming and does not add business value

Faster Delivery

• Go to market today, not in months: omega|ml’s unique models-as-data deployment strategy means any data scientist can deploy a machine learning model without needing hard-to-attain know-how in distributed systems or software engineering. Deployment is instant and takes just a single command, a single line of code.
• Leverage existing skills & tools – omega|ml data science workbench works with any data science toolkit and analytics platform utilizing the renown Python data science stack. Widely used frameworks like Pandas, scikit-learn, Tensorflow, PyTorch, Keras, Plotly and other frameworks are first-class citizens in omega|ml.
• No cloud vendor lock-in, full independence – omega|ml’s open-source core ensures a transparent, flexible approach to mix & match any private or public cloud. There is no lock-in to any particular platform.

Reduced Cost

• Remove engineering overhead – omega|ml’s ready-to-use, cloud-native end-end collaboration environment for data scientists and software engineers is built for use from development on a laptop to production-scale deployments in the cloud.
• Use the same infrastructure in development and production – Thanks to omega|ml’s multi-environment architecture and by using Kubernetes, leverage the same cloud resources for development, test and production while keeping these environments safely separated.
• Get rid of costly in-memory solutions – thanks to omega|ml’s integrated smart analytics storage, built on MongoDB, omega|ml removes the need for expensive in-memory clusters that other solutions require.

• Live dashboards – Data scientists communicate data insights by publishing real-time dashboards that seamlessly integrate data streams and machine learning models
• Ready for business process integration – Software engineers leverage production-scale integration APIs using the technologies already well known to them.
• No additional engineering skills required – Any data scientist can work with omega|ml by applying their existing skill set, freeing up time to add business value.

• Analytics without software engineering – Production-ready deployment of model and data pipelines takes just a single line of code, removing complex development work
• Fully leverage the flexibility of the cloud – Data scientists use cloud compute and storage resources as needed and from within their lab environment
• No vendor-lock in – Thanks to its open architecture, omega|ml can be integrated with any existing or new storage, data processing, or machine learning framework, compute backend or cloud environment.