Resource-intensive to maintain models
To train and put machine learning models into production, SEGES Innovation had a solution that was partly on-premise, used a lot of different tools and had a large manual code base.
This meant that the data science team spent a lot of resources on maintaining and retraining the models, and that there was a long way from model development to production.
“We spent many hours maintaining and retraining the existing models. That’s why we wanted to build an AI platform based on best practice MLOps principles. By focusing on automated workflows, we have been able to free up a lot of time, which we have instead spent on developing new data science products that can create value for users,” says Lasse Rose Malskær, Lead Data Scientist at SEGES Innovation.
Therefore, they decided to implement a new AI platform with a focus on minimizing maintenance time. They would achieve this by:
- Prioritizing simple solutions
- Outsourcing maintenance of the platform to PaaS
- Automated processes rather than manual processes
“We had the opportunity to start over and rethink how we could build a new AI platform from scratch so that it suited our needs. Here, Microsoft Azure (Machine Learning) was the optimal solution for us, and we chose twoday kapacity as a collaboration partner due to their extensive experience with Azure,” Lasse Rose Malskær says.