Eyad Sibai
Community@eyadsibai
Software engineer building reliable machine learning pipelines. Analyzing and modeling data whether it's in motion or at rest, in-memory or distributed
Agent Skills by Eyad Sibai
Showing 113 vetted skills indexed across 1 GitHub repositories.
eyadsibaivector-databases
Compare Chroma, FAISS, Qdrant, Pinecone, and Weaviate for vector database selection.
eyadsibaivisualization
Recommend Python visualization libraries and chart types for datasets.
eyadsibainetworkx
Create, analyze, and visualize network graphs using Python and NetworkX.
eyadsibaipymc
Build Bayesian models and run MCMC sampling with PyMC.
eyadsibaishap
Compute Shapley values to attribute feature importance for machine learning predictions.
eyadsibaidask
Parallelize Python workflows with Dask DataFrames and Arrays.
eyadsibaidata-science
Provide Python code for statistical modeling, A/B testing, and feature engineering.
eyadsibaigeopandas
Analyze and manipulate geospatial vector data with Python.
eyadsibaiml-engineering
Deploy and monitor machine learning models in production with Docker and Kubernetes.
eyadsibaihuggingface-tokenizers
Trains custom BPE and WordPiece tokenizers using HuggingFace tokenizers library.
eyadsibaimultimodal-models
Access pre-trained CLIP, Whisper, and Stable Diffusion models for vision, audio, and cross-modal tasks.
eyadsibaillm-inference
Compare inference engines like vLLM and llama.cpp for LLM serving.
eyadsibainemo-evaluator
Evaluate LLMs with MMLU, HumanEval, and GSM8K benchmarks.
eyadsibairag-frameworks
Compare RAG frameworks like LangChain and LlamaIndex for document QA.
eyadsibaiscientific-computing
Provide Python libraries for astronomy, bioinformatics, symbolic math, and statistics.
eyadsibaidata-engineering
Design data pipelines and warehouses with Airflow, Spark, dbt, Snowflake, and BigQuery.
eyadsibaitransformers
Run HuggingFace Transformers pipelines for NLP, vision, and audio tasks.
eyadsibaiexperiment-tracking
Track machine learning experiments with hyperparameters, metrics, and model artifacts.
eyadsibaimodal
Deploys Python functions requiring GPU execution to the cloud via Modal.
eyadsibaillm-training
Compare frameworks and techniques for training and finetuning large language models.
eyadsibaipolars
Perform efficient DataFrame operations on large datasets using Polars.
eyadsibaiscikit-learn
Guide scikit-learn algorithm selection, pipelines, evaluation, and hyperparameter tuning.
eyadsibaicontent-research-writer
Conduct research, add citations, and improve hooks for written content.
eyadsibaitech-stack-evaluation
Evaluate technology stacks using a weighted decision matrix and comparative data.