🤓 Jules Belveze
┣━━ 📦 Open Source
┃   ┣━━ tsa                                - Dual-attention autoencoder
┃   ┣━━ bert-squeeze                       - Speed up Transformer models
┃   ┣━━ bundler                            - Learn from your data
┃   ┣━━ nhelper                            - Behavioral testing
┃   ┗━━ time-series-dataset                - Dataset utilities
┣━━ 👍 Contributions
┃   ┣━━ 🤗 Hugging Face Ecosystem
┃   ┃   ┣━━ t5-small-headline-generation   - t5 for headline generation
┃   ┃   ┗━━ tldr_news                      - Summarization dataset
┃   ┣━━ ❄️ John Snow Labs Ecosystem
┃   ┃   ┗━━ langtest                       - Deliver safe & effective NLP models
┃   ┣━━  🧹 Dust
┃   ┃   ┗━━ Dust                           - Customizable and secure AI assistants.
┃   ┣━━ 💫 SpaCy Ecosystem
┃   ┃   ┗━━ concepCy                       - SpaCy wrapper for ConceptNet
┃   ┣━━ bulk                               - contributed the color feature
┃   ┗━━ FastBERT                           - contributed the batching inference
┗━━ 📄 Blogs & Papers
    ┣━━ Atlastic Reputation AI: Four Years of Advancing and Applying a SOTA NLP Classifier
    ┣━━ Real-World MLOps Examples: Model Development in Hypefactors
    ┣━━ LangTest: Unveiling & Fixing Biases with End-to-End NLP Pipelines
    ┣━━ Case Study: MLOps for NLP-powered Media Intelligence using Metaflow
    ┣━━ Scaling Machine Learning Experiments With neptune.ai and Kubernetes
    ┗━━ Scaling-up PyTorch inference: Serving billions of daily NLP inferences with ONNX Runtime

I currently work as a Software Engineer at @Dust.

My previous experiences include leading AI developments and setting up entire AI infrastructures at Ava, as well as spearheading MLOps and NLP projects at John Snow Labs. I have engineered multilingual NLP solutions at Hypefactors and conducted deep learning research at Microsoft.

I believe that automating model development and deployment using MLOps enables faster feature releases. To achieve this goal, I have worked with various tools such as PyTorch Lightning, FastAPI, HuggingFace, Kubernetes, ONNXruntime, and more.

Apart from this, I have worked extensively with Deep Learning and Time Series, completing my Master's Thesis on Anomaly Detection in High Dimensional Time Series. Additionally, I am keenly interested in exploring state-of-the-art techniques to speed up the inference of Deep Learning models, especially Transformer-based models.

I am an avid open source contributor and advocate for ethical AI practices.