Data Scientists

Stay informed and focus on

values derived from data

In the past, there was a lot of manual work of setting the environment, which is a fragmented and time consuming analysis process. And it is hard to collaborate with a team on the same project.

Now, you can carry out data analytics and optimizations with ML easily, and contribute your time on what really matters.

In the past, there was a lot of manual work of setting the environment, which is a fragmented and time consuming analysis process. And it is hard to collaborate with a team on the same project.

Now, you can carry out data analytics and optimizations with ML easily, and contribute your time on what really matters.

Let us handle your complex works and pressure

PrimeHub is a centralized platform for developing, delivering, and monitoring models and jobs with full governance and transparency. PrimeHub provides a robust machine learning infrastructure to run job submissions and manage model deployments in one place, so that data scientists can stay informed and focused on training and running their models.

With our solution, you will...

Innovate quickly by launching custom environments in seconds

Data scientists can provision resources with a single click. PrimeHub enables you to work with the most popular data science tools such as Jupyter, so you can spawn your own environment in sceonds with our Jupyter spawner. Our platform also provides a consistent environment and gives you the ability to productionize your projects.

Transparency and capability for

model deployments

PrimeHub's Model Deployment features enables data scientists to create, run and monitor new models with a few simple clicks. Gain visibility to each model to see how it runs and edit each one as needed.

Submit and run jobs at maximum efficiency

Data scientists sometimes have time-consuming tasks that run sequentially. Since such tasks take considerable time to complete, users are not able to engage with the entire progress. In this case, Job Submission can be used to create jobs consisting of sequential tasks and submit them for execution in background as data scientists monitor the progress through the logs.

Using job scheduler to reduce resources wasted

For routine jobs that need to be run on a schedule, schedule them with the Job Scheduler to avoid delays caused by re-implementing work in production systems. In this way, your business gets value faster, your resources aren't wasted, and you can still monitor and iterate production models to drive greater impact.

The AI Center needs to build and deploy models quickly, and the PrimeHub solved our urgency by eliminating most cost spent on communications. It’s priceless.

Mr. Chung-feng Liu, Supervisor of the AI Center

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