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Arundo Model Management

An overview of Arundo's Model Management

 

Transcription of Arundo Model Management

 

Ellie Dobson: What we have in Arundo is we have a mechanism for a data scientist to be able to get their model in front of the person who is making key decisions at the right time in the right format.

 

Cody Falcon: What we found out was there's a massive disconnect between a data scientist and a business user, and often times, it is in this model's management piece, specifically model deployment.

 

Ellie: The data scientist will spend typically a lot of time trying to access the data.

 

Cody: Getting IT involved, getting applications development involved, getting the various data owners and groups’ access.

 

Ellie: Just phoning up the right people or finding out who is the right person to phone up.

 

Cody: Once the data scientist has deployed the model, there are often times a team of IT architects, engineers, developers. So, Arundo really bridges that gap.

 

Ellie: What we enable the data scientists to be able to do, is with one single command, to be able to spin up an API, and also if they choose, to be able to auto-render a front end on that API.

 

Cody: A data scientist sitting right down the hall for me working on a python model that he’s built can one-click deploy that, and in three and a half minutes, I’m logging into a web application to begin interacting with that analytic model.

 

Ellie: For asset-heavy industries, we have software that allows a data scientist to be able to get their models in front of the right people at the right time as quickly as possible, so then you can make a very quick assessment on what are the right use cases to be working on.

 

Cody: People are no longer working with one or two or three models. Dozens of models are really common. We even see folks out there in the thousands of models. So, monitoring those at scale and just speeding up that process, which tools like the Arundo software suite allow them to do, is move from, again, stranded model in a local environment, to deployed in a cloud environment with a web interface in minutes is really key. It allows these data scientist to short cycle the time to value or just move faster through these use cases.