Computer vision is a foundational element of smart factory solutions. At deepsense.ai we have created diverse AI-driven, automated computer vision-based solutions that undertake the most demanding challenges in many different production lines.
Generating images from text has been a subject of rapid development as it might provide significant enhancements for the solutions across various domains. This text describes a complete framework that allows text-to-image conversion by combining several machine learning solutions.
This post discusses choices DevOps faces in a fresh project, which can take advantage of the newest version of Elasticsearch (7.14) and how to grow such a cluster from PoC up to a fully fledged production cluster.
With machine learning-powered tools, brands can evaluate a campaign’s ROI by analyzing video coverage of sponsored events. Thanks to computer vision solutions, it is possible to precisely determine the time and place a brand was positioned during a sporting event.
Read deepsense.ai’s article for AI Trends about leveraging the full potential of enterprise AI.
Read deepsense.ai’s article for MIT Sloan Management Review about artificial intelligence as the key to building customer loyalty in platform businesses.
Much has been said about the effective running of machine learning projects. However, the topic keeps coming up. It is vital to remember that the purpose of ML projects is not modeling itself, but achieving defined business goals.
As predicted by McKinsey experts, the technological revolution in the insurance industry has already started, and its progressive development will lead to a massive technological shift over the next decade.
Cost of risk is one of the biggest components in banks’ cost structure. Thus, even a slight improvement in credit risk modelling can translate in huge savings. That’s why machine learning is often implemented in this area.
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