Seamless integrations to empower your workflow
UbiOps supports teams to configure and build their own AI stack. It allows you to connect to any data science tools or data sources that can be accessed via APIs.
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Connect to your preferred databases
Deployments can also be used to connect to external data sources. We provide template deployment packages written in Python for connecting to databases, cloud services and other software systems. These templates can be used without modifications, but can also be customized for more advances use cases. All connector templates are published on our Github.
Model training and experimentation
Example integration: use training insights from Weights & Biases, and the compute resources and deployment possibilities from UbiOps to create a live and scalable model. Make use of UbiOps for model training, hyperparameter tuning and running inference and connect with W&B for experiment tracking, model evaluation and comparison.
Data Storage
Files are organized inside buckets. UbiOps has a file system that allows you to either create storage buckets directly on UbiOps, or connect to your own storage buckets on Google, AWS, Azure or any other provider that offers S3-compatible object storage.
You can have multiple buckets per project and there always is a default
 bucket in your UbiOps project.
Low code platforms
UbiOps can be integrated with low-code platforms. This is a list of articles that show how that could be done: Integrations
CI / CD - continuous integration and continuous delivery/deployment.
Configure CI/CD easily. Example? Using this tutorial you can set up a GitLab CI/CD or a GitHub Actions workflow that pushes your code to your UbiOps deployment every time you push a commit to the main branch of your repository.