More and more organisations – small and large – are coming out of the Proof of Concept phase of developing data models.
They’re now up to the task of bringing these models to operation and let them create the promised value.
- To be able to get data models to work organizations need next to data scientists, engineering capacity and expertise (DevOps).
- From many examples and references the Data Science vs. DevOps ratio is normally 5:1. This means you need for every 5 Data Scientists 1 DevOps engineer in your Analytics team.
- Many companies, however, struggle to find the right DevOps expertise. Talent is scarce and above all costly.
- For this reason most of data science models never make it to production.
Customer Case with Xenia
“With Xenia we managed to significantly reduce the time and efforts needed for deploying and managing our AI models for detecting failures of our switch heating installations.”
Cees-Jan Mas | Projectmanager Vernieuwing at ProRail
ProRail, a customer of Xenia, has spent only a factor 25:1 on ML vs Ops. In other words, ProRail spent effectively 96% of her time on developing the machine learning algorithms, while it took her only 4% to operationalise and maintain it.
For more information about ProRail’s customer story click here.
So what, you might think?
In the case of ProRail, going from a 5:1 to 25:1 ratio means that ProRail can scale her organisation from 5 to 25 data scientists with just 1 DevOps engineer, instead of 5.
At a scale of 25 data scientists, that means a yearly cost reduction of EUR 280.000. This is based on the average salary of 70k for DevOps engineers in The Netherlands (including 30% employer charges).
To help your organisation we have built a ROI Calculator, to find out whether building it yourself or use a platform like Xenia is the best alternative in your situation.
Speak to one of our specialists
If you have any questions about Xenia or if you would like to receive a demo about our platform talk to one of our specialists by contacting us