UbiOps Reviews

User friendly approach, and clear ops which is great

There are no negatives or cons of the UbiOps, they know what they are doing.

It helps me streamlining my ML pipelines and managing the workflow.

The best part about UbiOps is that it can turn your Machine Learning Models into scalable, robust and end-to end deployed apllication. Basically it requests the handling of the model and automates the scaling. It transforms Python/R code to web services and those also being microservices and help to manage resources and security as well.

Receiving notifications timely has been an issue with UbiOps as the reports regarding pipleine fail/crash does not arrive in time on emails. Also sometimes it becomes difficult to navigate through some areas as a new user as not much resources are provided regarding how to operate the tool.

If you are a data scientist or aspire to become one, the UbiOps is a must tool to have to supplement daily machine learning tasks. UbiOps takes care of everything for you be it deployment, code management, or maintenance for your machine learning models. We can also make parallel deployments and can train, test and validate our models on the go with amazing speed and support.

Doing deployment at a code level is always very hard for any data engineer or person working with Machine Learning Models and UbiOps makes deployment of ML models cakewalk. Version controlling provided by the tool and all the functionalities provided also cuts a lot of pain regarding code management. I have relaised that end-to-end deployment of services and maintaining models becomes very manageable with it.

There are many points why to use it because of its budget-friendly, after using it it seems like it is built for yourself. It enhances the process and its security feature is pretty good.

we don't get any notification when any model or pipeline crashes on my email which is disliked most otherwise it is very good to use it sometimes we face some problems in navigating.

It contains a large set of tools, libraries, and framework which is available to develop machine learning, data science work. We can perform continuous monitoring and testing of the models.

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.

Limits in number of users. No persistent blob storage possibility right now.

Easily deploying data science code, creating APIs.

- Easy packaging of scripts / libraries

- Trims down on development time by creating a common interface / platform for all models to be running in.

- Networking and data routing between models is managed by the pipelines. This means that the units of code deployed can be significantly simpler, which cuts down on both development and testing.

- Data exchanged across models can be a mix of structured, unstructured or binary blob data. It's quite flexible.