Easily run, manage and scale your data science models.

Turn your AI & ML models into powerful services with UbiOps

For any AI application

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.

Serverless ML inference

Deploy pre-trained machine learning models as a microservice with their own API. UbiOps is built as a model management and inference platform with the highest reliability and security standards. Scale to and from zero and only pay for the time your model is active.

GPU accelerated deep learning

Deep learning models benefit from the powerful acceleration options that GPUs provide. In UbiOps you can use GPU on-demand so you only pay for what you use.

Training ML models in the cloud

UbiOps can run jobs taking up to 48 hours. A great way to offload long-running workloads on powerful cloud hardware. This makes UbiOps a great platform to run training jobs for machine learning models. Choose between CPU instances or GPU acceleration.

Real-time computer vision applications

Computer vision is one of the major use cases for AI & ML, but running these models at scale can be challenging. UbiOps is built to handle a lot of data and scale rapidly to ensure your CV application keeps performing at all times. Use the model API to call it from a mobile or web application.

Create RPA (Robotic process Automation) services

There are many tasks that can be automated with Python or R scripts. RPA and UbiOps is a great combination as you can use the platform to run RPA tasks automatically and as a service after the code has been written.

Schedule recurring analytics jobs

UbiOps not only supports real-time inference, but you can also schedule your code to run at specific times and intervals as a CRON job. This makes it great for running periodic analytics jobs and to create reports.

Long-running scientific calculations

Running compute jobs on local hardware can be too slow or limited in RAM and CPU or GPU power. You can use UbiOps to run workloads up to 2 days on a large variety of cloud instances so it’s always optimized for the job.

Run ML/AI models behind a low-code app

Low-code and AI is a great combination. Many low-code applications exist that could significantly benefit from machine learning. However, running ML models inside a low-code app can be hard. UbiOps can be your solution for this.
Logging

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

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

Pipelines let you create larger workflows by connecting different deployments together. This allows you to build larger, modular applications.

Audit Events

The audit events show all activity in your project. They provide you with a full audit trail of what has changed and when.

Request schedules

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.

Metrics

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

Automate your workflow using the UbiOps Python Client & CLI

Use our templates for Github Actions or Gitlab Pipelines to integrate with your CI/CD process.

Use the powerful UbiOps platform API, CLI or Python/R client.

 

Integrate with Weights&Biases, MLflow, Streamlit, Tensorflow and other tools…

Start deploying your AI & ML models today

Start now