AI can bring value across various industries, but it is only when you put AI into production, that it will add clear and measurable value. Are you looking for inspiration on how your organization can get started on the AI journey?
Earlier this year McKinsey wrote “Two years ago people were asking, What is AI? A year ago, people wanted to learn how to trial minimum viable AI-fueled products. This year, the question is focused on how to get more value from AI”. So where to start if you want to achieve clear and measurable value with AI?
Teknologierne bag det hypede begreb gemmer uden tvivl på et betydeligt potentiale, men glem den dystopiske vision om, at vi i nær fremtid skal sige farvel til alle medarbejdere. Kunstig intelligens er automatisering på steroider, men ikke et nyt mirakel.
Putting AI models in production is notoriously difficult. The challenges have many different nuances and summarising those in one short blog post is ambitious. Instead, this blog post shortly summarizes the fundamental challenges of productionizing AI models and how...
Artificial intelligence is a booming field of research getting a lot of attention in the media due to its impressive applications such as image recognition or self-driving cars. The topic is however usually depicted from two fallacious angles…
In this post, we will briefly recap on the advancements within the field of AI, where AI/optimization in manufacturing stand, use cases and what to consider when implementing AI.
The technological advancement of computer science and machine learning have led to great achievements in different aspects of our society and daily lives. However, with the ever-growing digitalization and mapping of our personalities onto the digital public, the topic of privacy is gaining huge popularity and importance.
MELDS is an abbreviation for the five components: Mind, Experimentation, Leadership, Data, and Skills. These five components are invented for management purposes and should be considered when embarking on an AI journey.
A modern data science platform should not have the focus of enabling everyone to build machine learning models. Instead, the focus should be on structuring the deployment process, enabling more transparent and governed models, usable from enterprise-wide applications.