MLOps simplified.
ML Pipeline ⇨ Data Product without the hassle
Stop building infrastructure, deliver business value today
Are you tired of wasting time and talent on complex infrastructure, integrating tools, and navigating fast-moving technology?
omega-ml is the code-first MLOps for Data Products platform enabling data science teams to operationalize AI solutions. With all MLOps features built-in, omega-ml removes engineering overhead and empowers data scientists to build and deploy production-grade, scalable machine learning pipelines quickly and efficiently.
Learn about omega-ml's approach to MLOps and how your team can be more productive.
How omega-ml empowers your team
-
Data product approach to deliver value fast
omega-ml recognizes that the ultimate delivery of a data science team is not just a model, but a complete data product. Including standard and customized APIs, experiment tracking, production monitoring, notebooks, dashboards & interactive apps.
-
Instant, seamless production deployment in a single command
omega-ml eliminates the typical challenges of deploying machine learning pipelines in a professional engineering environment by treating models as data, not code. Models are versioned automatically, deployed in seconds. No complex CI/CD required. Even in-production retraining is a breeze.
-
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
.png)
Delivery models
-
On-Premises for free (open core)
-
Managed Service in your public cloud account
-
Custom Deployment on-premises, private or hybrid cloud
Why omega-ml
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.
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
Deliver Data Products Quickly and Efficiently
omega|ml is your one-stop hub to build, productize and launch your AI Data Products
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, version and deploy your models and feature pipelines?
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.
Scale
Leverage cloud power:
The built-in compute cluster provides instant, no-hassle, scalable model training and prediction.
As a managed services provider or internal DevOps team, deploy omega-ml commercial edition to offer your clients a scalable MLOps Platform As a Service that fits right in with your operations setup.
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.
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 using the experiment backend, means data is easy to compare.📜
App
Deployment
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 📜
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 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)
