Tech blog

August 31, 2021

MLOps – Deploying a recommender system in a production environment

When developing an integrated ML system, surprisingly little amount of time is spent on actual model development. The majority of time is spent creating the right prerequisites for model deployment – that is MLOps. 

The following sections describe how we work with MLOps at Ahlsell. We’ll go through data infusion and processing, modeling, and evaluation pipelines as well as how we put it all together in an automated CI/CD pipeline.

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December 27, 2020

Graph neural networks

Graph Neural Networks (GNNs) are neural network architectures that learn on graph-structured data. In recent years, GNN's have rapidly improved in terms of ease-of-implementation and performance, and more success stories being reported. In this post, we will briefly introduce these networks, their development, and the features that have lead to their success.

We will dive deeper into three use-cases, citation networks and drug discovery, using the package Deep graph library (DGL), and e-commerce using Pytorch geometric.

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October 06, 2020

Enabling real time e-sport tracking with streaming video object detection

The esports industry has seen tremendous growth lately. Each tournament is streamed live and reaches several million viewers all around the world, increasing the demand for live updates of games, players, e.g. for live betting and more.

To improve the experience of watching these tournaments, Abios Gaming provides an API for live information on games, teams, and players. To strengthen Abios Gaming’s offer, and to enable real-time monitoring of esport games, Modulai joined forces with their tech team to build a deep learning object detection solution, to extract information on-the-fly from real-time video streams of gaming tournaments.

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September 09, 2020

Classification of hypoglycemia causes in blood sugar time series

We give an overview of a study conducted in collaboration with Daniel Espes and Per-Ola Carlsson at Uppsala University aiming to improve the treatment of type 1 diabetes.

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.

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