There’s so much you can build with UbiOps at your fingertips. Deploy your Python & R code on UbiOps and instantly scale in the cloud. Use it for real-time serverless inference or long-running jobs. You can build single services as well as large workflows. Choose between efficient CPU or accelerated GPU instances.
For any AI application
In the logs you can keep track of everything that is happening in your project. The logs are also your primary source of information for debugging if something goes wrong.
Deployments run your code in a scalable way as a containerized microservice. Each deployment has a unique API endpoint for receiving requests with data to process.
Pipelines let you create larger workflows by connecting different deployments together. This allows you to build larger, modular applications.
The audit events show all activity in your project. They provide you with a full audit trail of what has changed and when.
Do you have a model or pipeline that needs to run on a fixed schedule? No worries, just configure a request schedule and we’ll make sure it runs on time.
Quickly see how your models are doing and keep an eye on data traffic in your project. There are many more metrics on the monitoring page.
Built for data science teams
UbiOps automatically containerizes your code, creates a service with its own API and takes care of handling requests, automatic scaling, monitoring and security.
Turn your AI models into scalable microservices
Deploy your code in no-time with our easy-to-use browser interface, Python / R client or CLI.
- Manage all your models in one place with version control and revisions.
- Don’t worry about Kubernetes, Docker images, uptime, scaling, monitoring and security. Python or R experience is enough.
- Process any type of data: structured data, files, images, text, sensor data, and more.
- UbiOps supports both low-latency requests as well as asynchronous batch jobs. You can also schedule runs for deployments and pipelines.
Auto-scale with access to on-demand CPU and GPU compute
Ready to scale while paying only for what you use
- Deployments scale automatically with the number of API calls.
- Scale-to-zero functionality. Only pay when your deployments are running.
- Choose the compute instances to suit your model. Access to both CPU and accelerated GPU hardware.
- Run in public cloud, hybrid cloud or on-premise
Create and orchestrate workflows
Re-use and combine multiple deployments in a workflow.
- Each deployment in a workflow is an isolated service that scales independently. Improving the efficiency and scalability of your application.
- In workflows you have the option to bypass deployments and merge output from multiple deployments to one.
- Import/export pipelines directly and share them with your colleagues or other users.
- Each workflow gets a unique API.
Keep track of everything in one place
- View metrics on usage and performance
- Check if there are any issues with your deployments
- Set e-mail alerts and notifications
- Get insight into everything that’s going on with extensive logging.
- Use the UbiOps web interface, API, Python / R Client or CLI to automate your workflow
“The on demand offering of UbiOps ensures that there’s GPU availability, with the option to scale very rapidly. Also, with UbiOps’ scale-to-zero functionality we don’t need to pay for GPU resources if the application is not being used, e.g., off-season.”Dr. Alexander RothHead of Engineering - Digital Crop Protection at Bayer
“We can quickly conduct pilots, and if successful, deploy it in production. We use UbiOps for different types of scripts: data cleaning/reshaping, several regression models and one neural network.”Mark FolkersHead asset management at ASSET Rail
“Now we can analyze our massive datasets within an adequate turnaround time; on average the runtimes of our models are accelerated by 4.5 times.”Ruben StamData Scientist at BAM Infra Nederland
“The extensive API documentation gives us the ability to build pipelines from scratch, without having to go through a 3 week training program.”Hervé HuismanFounder & CEO at Gradyent