Pinned Repositories
arguseyes
arguseyes-demo
caboose
demodq
jenga
Jenga is an experimentation library that allows data science practititioners and researchers to study the effect of common data corruptions (e.g., missing values, broken character encodings) on the prediction quality of their ML models.
mlinspect
mlinspect allows the instrumentation and inspection of native ML pipelines written in Python with pandas, sklearn and keras.
ragbooster
schemapile
seqrec
serenade
Serenade is a low-latency session-based recommender system deployed in production at bol.com
amsterdata's Repositories
amsterdata/ragbooster
amsterdata/schemapile
amsterdata/demodq
amsterdata/arguseyes-demo
amsterdata/arguseyes
amsterdata/caboose
amsterdata/seqrec
amsterdata/.github
amsterdata/jenga
Jenga is an experimentation library that allows data science practititioners and researchers to study the effect of common data corruptions (e.g., missing values, broken character encodings) on the prediction quality of their ML models.
amsterdata/master-theses-2023
amsterdata/mlinspect
mlinspect allows the instrumentation and inspection of native ML pipelines written in Python with pandas, sklearn and keras.
amsterdata/serenade
Serenade is a low-latency session-based recommender system deployed in production at bol.com
amsterdata/data-preparation-2024-assignment-3-students
amsterdata/freamon
Freamon enables data scientists to automatically reconstruct and query the intermediate data from ML pipelines to reduce the level of expertise and manual effort required to debug this data.
amsterdata/retrieval_importance
Implementation and experimentation code for the paper on Improving Retrieval-Augmented Large Language Models via Data Importance Learning.
amsterdata/snapcase
Snapcase contains differential implementations of state-of-the-art kNN models for sequential recommendation which can "unlearn" user data in milliseconds
amsterdata/snapcase-demo