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In the field of computer vision, synthetic data generation is especially interesting since the number of relevant resources and tools have grown and improved significantly over the years. The development has not been in the field of machine learning though, but rather in game engines such as Unreal Engine, Blender and Unity. Often produced by professional designers, realistic scenes are produced offering great details.
In this blog post, we take a look at how explainability techniques can be used on a deep learning model predicting atrial fibrillation from sinus ECG (electrocardiogram) data.
In close collaboration with Ahlsell’s data scientists, the team developed an end-to-end pipeline handling data ingestion, data processing, and model predictions. The recommendation system consists of a collaborative filtering model as well as a content-based model.
The objective of the project was to create a machine learning pipeline to analyze images from power lines and detect lack of tension in guy wires. To solve this problem, the team broke down the problem in different stages and created a multi-model AI solution, in close collaboration with the client.
After moving from Stockholm to Gothenburg back in 2014, after working in machine learning in Stockholm for a few years, I realized that in Gothenburg very few companies were working with machine learning. So I joined a small meetup-group of people that discussed machine learning, and we had meetups with the few companies working within the field, and ML students from Chalmers and Gothenburg University.
Today, more than 5,5 million people worldwide live with type 1 diabetes and need insulin-based treatment to survive. When it comes to diabetes, there’s no “one treatment fits all”, therefore doctor’s need support in optimizing treatments for each patient.
The client is an online skincare clinic that offers a finely-tuned selection of professional skin care products and make-up. Central to their operation are the customized product recommendations customers receive from their experienced skincare therapists.
Every day, thousands of people suffer from stroke due to an underlying undiagnosed AF condition. Stroke often results in conditions such as paralysis or, in many cases, death. For this reason, precise and early diagnosis of AF is an essential part of stroke prevention.
In a case like this, careful balancing of model complexity and predictive performance is key to best meet Ahlsell’s needs. Higher predictive performance means customers getting recommendations better suited to their needs, indirectly increasing Ahlsell’s revenue. However, real-time, low-latency inference in the cloud is not free, and customer experience is negatively impacted by slow(er) load times.
Type 1 diabetes is one of the most common chronic disorders among children and adolescents but affects people of all ages and globally more than 5 million people are affected.
The client is an American software company that provides solutions to financial institutions. Their offer includes technology for compliance, credit risk, and lending solutions used to manage risk.
Currently, there exist ML models to predict different financial KPIs such as debt, profits, revenue, etc. of a company based on their historic values. The values of these fields are related and dependent on each other.
Among the objects to be tracked were various weapons and tools the players use in-game, as well as certain interactions. The system detects and tracks timestamped events in correspondence with the objects the players possess, or use, to provide a structured stream of events that captures essential information about the evolution of the tournament.