MLOps simplified.
One-stop AI delivery platform, all the features you need.
relaxED
DELIVERY
A single platform to deliver end-2-end, compliant ML & AI systems, not just models or chatbots
deploy
instantly
Deploy models, scripts, notebooks and pipelines with a single command, fully compatible with your DevOps & CICD
USE tools you already know
Ready-made, consistent Python runtimes for your projects. Customized to your requirements
KEEP CONTROL
NO LOCK-IN
Built on Python, works anywhere. Deploy on your laptop, on-prem VM or Kubernetes, or to your private cloud
Data Products for analytics teams on a mission
How it works
Easily deploy models directly from your notebooks, Python scripts and jobs.
The dashboard shows the live repository of all deployed artefacts - including models, data, scripts and jobs
Understand the REST API live and test running models, jobs and scripts directly.
Easily deploy models directly from your notebooks, Python scripts and jobs.
Integrated Platform designed to remove complexity
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One-step dev-to-prod promotion of models, datasets & apps
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Automated notebook scheduling & pipeline execution
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Easy namespacing to separate projects, environments, groups
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Seamless cloud integration and on-prem deployment
Deliver Solutions - not Technology
MLOps as it should be: Easy to start, fast to scale
omega-ml is the code-first MLOps platform, enabling even individual data scientists and small teams to operationalize AI solutions end-2-end. With all MLOps features built-in, omega-ml removes the typical engineering overhead.
Why MLOps simplified matters
Most data science teams are small, averaging 1 to 5 people, with no easy and fast 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.
Deploy Machine Learning without the Engineering Complexity
As a data scientist you already have to deal with a wide range of methodologies, tools and metrics. Your main focus should be to solve business problems, not to manage a complex technology stack. omega-ml removes the need for complex Machine Learning Engineering by providing an integrated platform so that all MLOps features and integrations just work, out of the box.
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One-stop platform to deliver value fast
omega-ml recognizes that the ultimate delivery of a data science team is not just a model, but a complete AI system. Including standard and customized APIs, experiment tracking, production monitoring, notebooks, dashboards & interactive apps.
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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.
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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.
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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.
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
"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 AI systems 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.
Data
Access
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. 📜
Model
Training
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.📜
Model
Repository
& Versioning
Every saved model is available from the integrated repository. Models are versioned automatically. Each version can be tagged (.e.g. "production"). 📜
Model
Serving
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. 📜
Live
Model
Tracking
Track all model predictions automatically and assess the model and data quality over time. Live tracking means to detect data and model drift automatically and early.
Apps
Deploy your own apps such as dashboards and full-fledged AI systems by simply saving the app's code, or referencing a git repository. commercial edition 📜
Collaboration
Easily collaborate with your team by using a common configuration. Security is built-in and supports SSO. commercial edition📜
Platform
Integration
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 Generative AI
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OpenAI - any OpenAI-compatible endpoint (completions and embeddings)
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vLLM - language models can be deployed locally
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LocalAI - language models can be deployed locally
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Huggingface - language models from hugging face can be packaged and deployed
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Gensim, Spacy - classic NLP pipelines can be packaged and deployed
Plugins for ML Frameworks
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Scikit-Learn - any scikit-learn model and pipeline
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XGBoost - all scikit-learn compatible models
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MLFlow - any MLFlow-compatible framework (Python), including packaged projects and models
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PyTorch - any custom PyTorch model can be packaged and deployed
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Tensorflow -
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R - any R scripts, models and dataframes compatible with reticulate
Plugins for Databases
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SQLAlchemy - any DBMS comptible with SQL Alchemy, e.g. SQL Server, Oracle DB, PostgreSQL + pgvector, MySQL
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Snowflake - access to retrieve and store data using SQL
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NoSQL document and BLOB storage (using MongoDB)
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S3/Minio - retrieve and store files
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HTTP and REST APIs - retrieve and store any data
Plugins for Deployment and Cloud
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Docker - the community edition runs on docker
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Kubernetes - all commercial editions run an K8s
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Exoscale - our go-to platform for European deployments
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Azure - commercial deployments for Azure clients
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AWS - commercial deployments for AWS clients
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K3s - Kubernetes edition for edge deployments
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Linux - any client and worker runtime deployment
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Windows - clients (full support), worker runtime (limited)
Why omega-ml omegaml
01.
Flexible
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.
02.
Fast
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.
03.
Scalable
Scale model training and prediction from any client, applying the power of the built-in compute cluster.
04.
Cost-Effective
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.
05.
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.
06.
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.