Making AI more accessible with AutoML

As more companies look to streamline their data workflow, they’re turning to machine learning to help automate these time-consuming processes.

A perfect example of this is NEM Forsikring, who have an overall strategy of being a proactive insurance company:

• With Kør Godt, their car insurance product, they aim to minimise the number of car accidents from happening by improving driving performance
• The solution is simple: by only using their customer’s smartphone and automated machine learning (AutoML), they can evaluate the driving performance of cars as well as give advice on safer driving

AutoML can handle complex and manual processes by putting the data into one single solution to reduce repetitive tasks and save time (and potentially lives!).

For this Tech Update, we are joined by Dan Rose, co-founder of Paperflow, an AI-powered scanning and processing solution, who will start us off with:

• What AutoML is
• What the pros and cons are
• How it makes ML and AI more accessible to everyone, not just data scientists

Nicholai Stålung, data scientist and machine learning lead at Trifork, will share his hands-on experience with AutoML at Kør Godt by Nem Forsikring, where he will cover:

• What the challenges and clear benefits are behind this solution
• How AutoML looks in production and how to start building the right platform for your business
• Where companies can leverage AutoML outside the insurance industry