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MLOps simplified. 

Deploy Your ML Pipelines as Data Products Instantly.

unique approach

Data product approach to deliver value fast 


Promote models and pipelines in one step

any ide

Ready-made, consistent & customized runtimes

start locally
scale to cloud

Built on Python

Works anywhere

Deliver Solutions - not Technology

MLOps as it should be: Easy to start, fast to scale.

omega-ml is the smart code-first MLOps for Data Products platform, enabling data science teams of all sizes to operationalize AI solutions end-2-end. With all MLOps features built-in, omega-ml removes engineering overhead. 

Why it matters.

Most data science teams are small, averaging 1 to 5 people, with no easy access to engineering capacity. omega-ml comes as a fully integrated platform and is ready to deliver production-quality pipelines, models, and solutions from day 1. 

One Platform, Many Use Cases.

Initially developed for internal use and published as open source in 2018, omega-ml has been used to accelerate AI projects of all types and scales, and across industries. Successful uses cases include startups in logistics, finance and marketing, consulting engagements in Europe and USA, as well to empower analytics teams in Swiss insurance and banking.

   omega-ml empowers data scientists from day 1

  • Data product approach to deliver value fast 
    omega-ml recognizes that the ultimate delivery of a data science team is not just a m
    odel, but a complete data product. Including standard and customized APIs, experiment tracking, production monitoring, notebooks, dashboards & interactive apps.

  • Instant production deployment in a single step  
    omega-ml eliminates the typical challenges of deploying machine learning pipelines by treating models as data, not code. Models are versioned automatically, deployed in seconds. Even in-production retraining is a breeze. No complex CI/CD required, enabled by smart engineering.


  • Code-first, consistent environments and open architecture
    omega-ml supports all popular data science tools and frameworks, enabling your team to use the existing code base without changes. Ensure consistency across your workflows by pre-built, readily deployed runtimes. Integrate with any DBMS or other third-party system thanks to an extensible Metadata and plugin system.


  • Scalable from laptop to cloud, enabled by open source
    omega-ml leverages widely used open-source technology (Python, RabbitMQ, MongoDB) and is straight-forward to deploy on cloud, on-premises, hybrid, and edge environments. Readily available for Kubernetes, Docker and IaaS deployments, omega-ml will make your platform engineering and DevOps teams happy.


Data Products for analytics teams on a mission

How it works

Python & R, command line (cli) and REST APIs to deliver value fast

  • Store and access features in any data source or DBMS

  • Train and version models automatically, locally, and in-cluster

  • Instantly serve models from ready-made REST APIs

  • Track live model metrics in training and in production

Integrated Platform designed to remove complexity 


  • One-step dev-to-prod promotion of models, datasets & apps

  • Automated notebook scheduling & pipeline execution

  • Easy namespacing to separate projects, environments, groups

  • Seamless cloud integration and on-prem deployment

With a single line of code omega-ml deploys datasets, models. They are instantly available from the REST API.

Delivery models

  • On-Premises for free (open core)

  • Managed Service in your public cloud account

  • Custom Deployment on-premises, private or hybrid cloud

Success Stories


"The traditional software delivery process does not scale for MLOps. omega-ml has increased efficiency in projects at Startups, Consultancies, Banks and Insurance Companies alike."

Founder omega-ml and 

Practice Lead MLOps 

Patrick Senti, Switzerland


"We rely on omega-ml to do the heavy-lifting for our solution deployment.  omega-ml enables us to focus on our core business - we’re just more efficient & effective!"


Founder & CEO at Syntheticus

Aldo Lamberti, Switzerland

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"We leverage omega-ml to operationalize our scientific efforts with ease. omega-ml has saved us a large part of development efforts to reliably serve our clients."


Head Business Development, 
a Scientific Startup, Switzerland

MLOps Features Your Team will ❤️

MLOps simplified means all features you can expect from an MLOps platform, included out of the box. Enabling your team to develop, launch and operate productive Data Products from day 1, omega-ml provides a single and easy to use Python API, a command line interface and a configurable REST API. Our minibatch streaming component enables asynchronous use cases without the need for additional infrastructure, reducing your team's learning curve and solution complexity. The 📜 icon links to the documentation for each feature.



Access and process any data from SQL DBMS, third-party APIs, S3/Minio, SFTP, HTTP Kafka and any custom data source using plugins. Streaming included. 📜

Feature Pipelines

& Store

Process data using the tools you known and like - you may use Notebooks, Python functions or full-fledged scripts 📜. Store features in the built-in zero-config DB or by connecting your DBMS.📜



Train models locally and in the cloud with the same code. Write your training pipeline as you always would, tracking metrics in convenient experiments.📜

Experiment Tracking

Initiate new experiments with just a single line of code and track any metrics, artifacts, inputs and custom data. Access metrics as convenient DataFrames.📜


& Versioning

Every saved model is available from the integrated repository. Models are versioned automatically. Each version can be tagged (.e.g. "production"). 📜



Instantly serve models from standard and customized REST APIs. Model serving is as simple as saving the model, once saved the model is available from the REST API. 📜




Track all model predictions automatically and assess the model and data quality over time. Live tracking using the experiment backend, means data is easy to compare.📜


Deploy your own apps such as dashboards and full-fledged data products by simply saving the app's code, or referencing a git repository. commercial edition 📜


Easily collaborate with your team by using a common configuration. Security is built-in and supports SSO. commercial edition📜


A fully-fledged platform, omega-ml commercial edition fits right in with your DevOps needs and platform engineering policies, while your data science teams stay focused on delivering business value, not technology. 📜


Any Framework, Any Platform

MLOps simplified means you can continue to use the same ML frameworks and databases that you already use and love. omegea-ml does not stand in your way, it simplifies the deployment and operations aspect. The following frameworks and platforms are supported out of the box. Any framework can be supported using plugins. Additional plugins can be provided on request. 

Plugins for ML Frameworks

  • MLFlow - any MLFlow-compatible framework (Python), including packaged projects and models

  • PyTorch - any custom PyTorch model can be packaged and deployed 

  • R - any R scripts, models and dataframes compatible with reticulate

Plugins for Databases

  • SQLAlchemy - any DBMS comptible with SQL Alchemy, e.g. SQL Server, Oracle DB, PostgreSQL, MySQL

  • Snowflake - access to retrieve and store data using SQL

  • NoSQL document and BLOB storage (using MongoDB)

  • S3/Minio - retrieve and store files 

  • HTTP and REST APIs - retrieve and store any data

Plugins for Deployment and Cloud

  • Docker - the community edition runs on docker

  • Kubernetes - all commercial editions run an K8s

  • Exoscale - our go-to platform for European deployments

  • Azure - commercial deployments for Azure clients

  • AWS - commercial deployments for AWS clients

  • K3s - Kubernetes edition for edge deployments

  • Linux - any client and worker runtime deployment

  • Windows - clients (full support), worker runtime (limited)


Why omega-ml



Leverage Python machine learning models & pipelines in any application, straight from our easy-to-use REST API. Easily add custom endpoints with automated data validation and compliant with Swagger/OpenAPI.



Collaborate instantly on any data science project, using the tools you already know and love (e.g. Python, scikit-learn, Jupyter Notebook). Instant deployment from a single line of code.



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



A single, integrated solution that covers all your MLOps needs. Easy to use for Data Scientists, a breeze to deploy for Platform Engineers and DevOps Specialists. 


No Vendor Lock-in

Our fully open source core and support for Linux, Docker and  Kubernetes cloud means you can deploy anywhere. Laptop, Cloud, On-Prem, Hybrid-Cloud or Edge.


Secure & Independent

Deployed in your Private Cloud or On-Prem, omega-ml meets all your data privacy and security requirements. The commercial edition provides SSO, a built-in secrets vault and  auditing to keep your data safe and under control.

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