Making AI more accessible with AutoML

Tue, September 29, 2020

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

Dan Rose

Paperflow

Co-founder & AI enthusiast

Dan is an entrepreneur and AI enthusiast who co-founded Paperflow (formerly known as Bilagscan) – one of Denmarks most successful machine learning based companies. Paperflow has created a best-in-class document data capture solution that is used in more than 25 countries today.

Today, Dan helps companies utilize the power of AI, mainly by educating and working with them on how to strategically use AI to improve business results. He speaks and writes about AI and how it can be applied in his blog.

Nicholai Stålung

Trifork

Data Scientist and Machine Learning Expert

Nicholai Stålung has a background in econometrics, mathematical modelling and financial technology. Nicholai is an experienced Data Scientist and is skilled in Python (Programming Language), Machine Learning, Deep Learning, Analytics and Statistics.

From his 5+ years of experience, Nicholai has gained a holistic view of Machine Learning and data projects which has given him the opportunity to lead several projects, from start to finish, within the most dominant industries in Denmark.

Nursel Yildirim

Invokers

Moderator and Head of Digital Design & Delivery

For over 15 years, Nursel has been working with digital transformation and has delivered some of the most popular digital solutions on the Danish market. She has led projects and managed teams within Business Development & Strategy, Project Management, UX, UI, Design Thinking, Software Development, QA, and Operations. She advocates and strives for creating user-centric digital solutions of high-quality and feels a great sense of accomplishment in doing so.

Nursel has an economics background and is currently Head of Digital Design & Delivery at Invokers, a Trifork Company. Her focus is to build user-centric enterprise solutions with the help of Design Thinking and high-end delivery methodologies.