NannyML

NannyML is an Open Source Python library to detect silent model failure, estimate the performance of ML model even without ground truth, and robustly identify data drift.

Co-Founder and CEO

Prophecy Labs

Founder

Data Solutions. Elegantly Crafted.

Prophecy Labs helps companies automate complex work, optimize business processes, and predict events that impact your business.

Let our passion, be your edge.

Managing Partner

Essent.be

Developing a data platform in Scala/Spark to deal with large scale data and
deploying large scale machine learning models.

Big Data Engineer

SCK-CEN

This project was to help prepare for long term space travel namely a long term lunar outpost within the following decade. It involved working with gene expression data to help determine the effects of long term isolation and harsh condition on the human immune system. Data from workers of the Concordia platform in antarctica was used. Technical details of the R code can be found at the link below.

Research Bioinformatician 

Building an open source library to estimate the performance of deployed machine learning models in the absence of ground truth.

I love talking about: machine learning, decision making, post deployment data science, performance estimation, and bunch of other stuff. 

currently learning about: causal inference 

Always open to have a chat 🙂

Leave me anonymous feedback -> https://www.admonymous.co/hakim_elakhrass

🥸Hakim Elakhrass

building Soda to monitor the worlds data and decisions

Doctoral Researcher (CS) at University of Helsinki

Last week at the NeurIPS 2025 presenting our paper "Estimating Model Performance Under Covariate Shift Without Labels", which is joint work with Jakub Białek, Wojtek Kuberski, and Nikolaos Perrakis. What a highlight reel of talks, presentations and discussions! For those who can't wait until the proceedings are published, check out the paper in arXiv at https://lnkd.in/dME4FhMT.

taplio