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.

Volvo Group

★ Enterprise AI Strategy Lead | Redefining AI Goals at Volvo

Senior Machine Learning Architect 

★ Designed and implemented an Enterprise MLOps framework that streamlined data science projects involving predictive analytics, NLP, and computer vision at scale, resulting in improved efficiency and faster time-to-market for models

★ Implemented an enterprise-scale MLOps central monitoring dashboard to track both Model and Data drift using statistical techniques

★ Led the development of data and model governance framework incorporating automated data lineage tracking, data quality checks, and model performance monitoring. Achieved a 35% increase in data accuracy and a 25% reduction in model downtime

Senior Machine Learning Engineer

Merck Group

★	Streamlined implementation process and reduced average waiting time on product status inquiry from 2 days to 60 minutes by implementing custom NER model (RoBERTa large); Increased Customer Satisfaction Score (CSAT) from 46 to 72 on consecutive consolidated quarters

★	Developed model for Invoice document processing by using OCR (CRAFT + Attention OCR ) and NER (XLM-RoBERTa) model yielding a reduction of 15 % operation time via process automation; Sales lift increase by 2.5% and Expand service to top-5 high net sales countries

★	Implemented 4 different Recommendation systems for the scientific community research portal by amalgamating scientists and experts into a single platform; Stimulated an additional 30% lead generation and increased click-through rate by 18%

★	Designed sequence prediction model for chemical mixture separation using CNN BiLSTM model architecture; Resulted in $850k savings including annual licensing cost by developing the in-house product; Improved performance by 15% compared to the existing product (Empower Software)

★	Created a web scraping program in Python to support downloading public data, included >10,000 scientific abstracts and chemical products combined enriching internal data and increasing research efficiency by >50%

★	Research and Interpret to prototype to identify key influencers (HCP) on sales data using clustering techniques over 100,000 data points in Python

Senior Data Scientist

Miracle Software Systems, Inc

★	Achieved 82% accuracy by predicting Car Exterior Damage with trained on 1000 annotated images using Mask R-CNN architecture to speed up insurance claims; Reduced claim processing time by 40% and claim costs up to 25%

★	Conceptualized the real-time shopper footfall in retail stores by analyzing demand and visitors sentiment patterns using Faster R-CNN model architecture; Improved net promoter score by 30 points in pilot teams over 6 months; Understood customer sentiment and demographics by 25% than previous 12%

★	Customized Brand logo detection in commercial ads video using YOLOv3; Reduced time consumption by 70% time spent by the team

Data Scientist

High energy professional specialised in Machine Learning, NLP, CV and MLOps, with a proven track record of executing successful strategies. With exceptional analytical skills and strong business acumen, I have consistently delivered results that exceed expectations. 

Let's connect on LinkedIn to explore how I can bring my expertise to help drive your business forward.

My technical skills set include:
•	Domain: MLOPS, NLP, Computer Vision (CV), Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), Data Science
•	Architecture/Algorithms: Transformers, BERT, RoBERTa, CNN, RNN, LSTM, ULMFiT, Transfer learning
•	Library/Framework: PyTorch, TensorFlow, Keras, FAISS, Spark NLP, flair, Scikit-Learn, selenium, Tesseract, Flask, Neo4j
•	Web/Deployment: MLOps, Docker, Flask, Streamlit, RESTful API, Git, CI/CD
•	Cloud: Hugging Face, Amazon Web Services(AWS), Microsoft Azure

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