Introduction to UbiOps¶
UbiOps is developed for data scientists and teams who are looking for an easy and production-ready way to deploy and run their Python or R models as live services in the cloud.
UbiOps takes care of containerization of your code, deploying it as a microservice with its own API endpoint, request handling and automatic scaling. There are also advanced features for creating data pipelines, version management, job scheduling, monitoring, security and governance.
Deployments are the entities in UbiOps that serve your models, functions and scripts for data processing. They can receive requests through their API endpoint to process data. Deployments run in containers tailored to the needs of your code. UbiOps offers runtimes for both Python and R. Deployments also have versions which you can use to keep track of model updates. Read more->
A Pipeline is a sequence of Deployments. In a pipeline, you can connect the output of a deployment to other deployments. Pipelines have their own individual API and can receive requests just like deployments. UbiOps will manage the data flow. Read more->
Requests A Request can be sent to both Deployments and Pipelines. It will trigger a single run of a deployment or pipeline using its data payload. You can send requests to the API endpoint of a deployment or pipeline. You can also schedule their execution. Read more->
The best way to learn about UbiOps is to start using it. We have several resources to get you started:
- The Deployment quickstart shows you everything you need to know to deploy and serve your code on UbiOps.
- The Pipeline quickstart shows how to define and run a dataflow in UbiOps consisting of several Deployments.
- The UbiOps tutorials is here to provide users with inspiration on how to work with UbiOps with ready-to-go deployments and notebooks.
Ways of using UbiOps¶
There are multiple ways to interact with the UbiOps platform and API:
Command Line Interface
With our Command Line Interface you can interact with the UbiOps platform API from your terminal.
All of these options offer access to the same functionality. You could, for example, create a new project using the Command Line Interface and edit it afterwards using the WebApp.