UbiOps | FutureHurry
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Main Purpose

UbiOps is a platform designed for data scientists and teams to easily deploy, train, and run their own machine learning (ML) and data science code, as well as deploy off-the-shelf models and run data processing tasks.

Key Features

  • Deployment: Deploy ML models, functions, and scripts as microservices with their own API endpoints.
  • Pipelines: Build workflows based on deployments to create data pipelines.
  • Requests: Trigger single runs of deployments or pipelines using data payloads.
  • Experiment: Define training setups, including environment, instance type, and storage options.
  • Training Runs: Execute multiple training jobs in parallel within an experiment.
  • Environments: Manage code and environment separately, reuse environments for different deployments and experiments, and customize environments with additional dependencies.
  • WebApp: Access UbiOps through an easy-to-use web application.
  • API: Use the platform API directly to interact with UbiOps.
  • Python and R Client Libraries: Integrate UbiOps API methods in your code and automate workflows using Python or R client libraries.
  • Command Line Interface: Interact with the UbiOps platform API from the terminal.

Use Case

  • Deploying and serving machine learning models.
  • Running data processing tasks.
  • Building data pipelines.
  • Training and experimenting with machine learning models.
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Pricing Model:

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