If you are a data scientist or engineer, at some point you want to bring your algorithm to production. And that means installing libraries, managing dependencies, deploying your scrips and models, versioning, serving, and running out of compute.
Let’s be honest: deployment is hard. The tools we use are not as helpful as they could be, because they are not designed for our specific needs. And we lose ourselves in time-consuming model deployments and infrastructure management.
That is not what we are meant for. We want to make sure that our time is best spent where we are needed, developing algorithms and code to create impact.
That’s why we’re building UbiOps.
User in Construction
Advanced user of UbiOps
★★★★★
UbiOps enables us to develop, deploy and operate any type of data science code.
What do you like best?
We chose to work with UbiOps because of its simplicity and speed, and because it can be integrated with the existing analytics platform. With UbiOps, they can deploy data science code immediately. APIs of the models are automatically created so they can make requests to it and bring the model live without having to worry about the IT. Furthermore, good and quick support.
What do you dislike?
Limits in number of users. No persistent blob storage possibility right now.
What problems are you solving with the product? What benefits have you realized?
Easily deploying data science code, creating APIs.
Review source: G2.com
