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Get real time insights into supply and demand and state of infrastructure

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With Machine Learning, utility operations can be supported by:

monitoring and proactively maintain infrastructure like pipelines or electricity lines
modelling renewable energy supply based on weather data and cloud coverage
modelling energy demand for most economic production capacity utilisation

Projects in Utilities

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Automatic Planning of Solar Systems

Creative solutions enabled us to automate the process of planning solar systems

Defect Detection in Manufacturing

AI-supported optical defect detection for semiconductor laser production

Smart Access Control with Facial Recognition

We developed a multi-level security system with facial recognition for automatic access control.

Blog Posts in Utilities

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Natural Language Processing

GPT-3 and beyond - Part 1: The basic recipe

By Fabian Gringel September 27th, 2021

In this blog article I will explain how GPT-3 works, why some people think it’s dangerous, and how you can try out a GPT-3-like model yourself for free.


21 questions we ask our clients: Starting a successful ML project

By Emilius Richter May 21st, 2021

We discuss what questions should be considered and answered up front to launch a successful machine learning software project.

Computer Vision

Pretraining for Remote Sensing

By William Clemens (PhD) May 11th, 2020

In this blog post I will describe a number of pretraining tasks one can use either separately or in combination to get good “starting” weights before you train a model on your actual labelled satellite/aerial images.

Theory & Algorithms

Machine Learning Approaches for Time Series

By Machine Learning Approaches for Time Series December 18th, 2019

This blog post provides an introduction into time series and serial sequences and shows how Recurrent Neural Networks (RNN) can deal with them.

Computer Vision

Semantic segmentation of satellite images

By Nelson Martins (PhD) May 24th, 2019

Setting up a ML model for the semantic segmentation of satellite images, we found that a UNet architecture with dice loss enforced with a pixel weighting strategy outperforms cross entropy based loss functions. Here we present the approach and the results.

Use Cases in Utilities

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