Python on an M1 chip: Running smoothly using Docker 02/14/22 by Denis Stalz-John I have been working as a data scientist at codecentric for several years now. Thus, my language of choice is... Read more Leave your thoughts
BigQuery to the rescue: How to prototype an ML system for a medium-sized wholesale company in the Google Cloud 02/02/22 by Felix Medam BigQuery can help with building an ML system for production with a short time to market. Follow industry standards. Agile... Read more Leave your thoughts
Evaluating machine learning models: Establishing quality gates 02/01/22 by Berthold Schulte The quality or usefulness of machine learning models can be evaluated using test data and metrics. However, to what extent?... Read more Leave your thoughts
The universal recommender in Action(ML) 04/19/21 by Francesca Diana This article presents a new collaborative filtering technique. The universal recommender can be run as “engine” of ActionML's Harness. Read more Leave your thoughts
NER with little data? Transformers to the rescue! 12/14/20 by Thomas Timmermann See how to fine-tune a pre-trained BERT transformer on a custom NER task, beating standard bi-LSTM thanks to more learning transfer! Read more Leave your thoughts
Take control of named entity recognition with your own Keras model! 11/13/20 by Thomas Timmermann We build, train and evaluate a bidirectional LSTM-network for named entity recognition to extract information from legal texts with Keras. Read more Leave your thoughts
NER @ CLI: Custom-named entity recognition with spaCy in four lines 11/06/20 by Thomas Timmermann We show how to extract information from legal texts, training spaCy's named entity recognition model for our task on the command line. Read more Leave your thoughts
Why user-oriented development is so important – the story of tactics.ai 08/24/20 by Denis Stalz-John and Leonie Günther In this blog post, we want to give you an insight into the product development of tactics.ai. Our initial idea... Read more Leave your thoughts
Thinking AI means re-thinking data 05/28/20 by Marcel Mikl While doing AI is sexy and cool, data infrastructure is not considered any of this, even though machine learning applications rely on it. Read more Leave your thoughts