Resources /

The AI productivity challenge: 2nd edition

February 20

We published the first edition of this paper on how to drive productivity in the areas of machine learning and deep learning in 2019. What has changed since then? How is the way forward looking now? Read on about this second edition and download your copy at the bottom.

Artificial intelligence, in particular the subfields of machine learning and deep learning, have shown remarkable progress in recent times. There is a general consensus that over time every business will need to start using AI if they are to be competitive and successful. AI is not something for the future nor something only for the large technology titans but rather a technology that is increasingly entering the mainstream.

The data scientist has emerged as an essential resource.

Björn Brinne, Head of Data Science at Peltarion

  • Björn Brinne

    Björn Brinne

    Head of Data Science

    Björn Brinne has over a decade of experience working in data science at companies such as Truecaller, King and Electronic Arts before joining Peltarion. He holds a Ph.D. in theoretical physics from Stockholm University and has contributed to many research papers across a range of academic fields, including computer science, string theory and computational biology.

Download your copy here

This is an updated second edition of an introductory paper on how to drive productivity in the areas of machine learning and deep learning. The paper highlights some of the key barriers to getting good outcomes in these projects and then outlines some potential functions or capabilities organizations should consider as they look at technologies to help support their AI programs, updated to the developments since the time of the first publishing. Fill in the form to download the paper.