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
The solution’s features include the full stack of analytics operations: data ingestion, analytics storage, parallel and fast big data processing, machine learning model training, serving as well as API integration, security and monitoring. Unique features include
- One-stop integrated platform – all features needed for effective analytics from development to production, enabling collaboration from anywhere
- Instant deployment and versioning – datasets, models, apps, streams and pipelines are deployed in seconds; every machine learning model is automatically versioned thanks to the unique ‘models as data, not code’ approach
- Any-size datasets with Python-native processing – structured or unstructured data, from tabular to images
- Extensibility – omega|ml provides a unique plugin-system that makes it a breeze to integrate third-party components – most of the standard features are built as plugins.
omega|ml is offered as a managed cloud-service or on-premise.