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I'm Safoine El khabich

Machine Learning Engineer šŸ‘Øā€šŸ’» Passionate about MLOps, Distributed Systems and Open source šŸ¤–


  • šŸ”­ Iā€™m currently working on MLOps Framework šŸ‘‰ ZenML Core Team

  • šŸŒ± Iā€™m currently learning AWS šŸ“˜

  • šŸ‘Æ Iā€™m looking to chat/collaborate on Anything related to ML, MLOps, Cloud. šŸš€

  • āš” Fun fact: Tidy laptop, messy desktop is my life slogan. šŸ˜›

  • šŸ“« Reach out to me [email protected] or [email protected] šŸ™


Connect with me


Recent Blog Posts

  • How can MLOps solve the problem of Production in Machine Learning? Article

  • All Continuous, All The Time: Pipeline Deployment Patterns with ZenML Article


Safoine El Khabich's Projects

actions-cache-s3 icon actions-cache-s3

Cache dependencies and build outputs in GitHub Actions (support S3 backend for @actions/cache)

automatic-number-plate-recognition icon automatic-number-plate-recognition

This repository for project that can Automate Number Plate Recognition (ANPR) in Morocco Licensed Vehicles. šŸ’» + šŸš™ + šŸ‡²šŸ‡¦ = šŸ¤– šŸ•µšŸ»ā€ā™‚ļø

kserve icon kserve

Serverless Inferencing on Kubernetes

mlops-stacks icon mlops-stacks

A series of Terraform based recipes to provision popular MLOps stacks on the cloud.

mlops.toys icon mlops.toys

šŸŽ² A curated list of MLOps projects, tools and resources

mmengine icon mmengine

OpenMMLab Foundational Library for Training Deep Learning Models

mnist-mlflow-dvc-argo icon mnist-mlflow-dvc-argo

This Project is part of an MLOps stack am trying to create and test using (Kuberntes, Argo, MLFlow, DVC ...)

moroccoai-mlops-series icon moroccoai-mlops-series

This a series of courses/presentations and workshops about Machine learning in production or ML Operationalisation (MLOps).

version-controllers-git icon version-controllers-git

This repo is made as result of the work done for a giving presentation about version controllers and git.

yolov5 icon yolov5

YOLOv5 šŸš€ in PyTorch > ONNX > CoreML > TFLite

zenml icon zenml

ZenML šŸ™: MLOps framework to create reproducible pipelines.

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