Hello! I'm a passionate Data Scientist and Machine Learning Engineer currently pursuing a Master's in Machine Learning Engineering (Pos-Tech / FIAP). I build end-to-end ML systems, production pipelines, generative AI applications, and scalable data solutions — from experimentation and model validation to MLOps, feature stores, and deployment.

I enjoy breaking things to understand them deeply (skew, leakage, latency, drift...) and documenting the fixes. Open-source learner & builder.

• Languages: Python, C#, C++, SQL, Scala, DAX, M Language
• ML/DL Frameworks: Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, NumPy, Pandas, Polars, Catboost...
• MLOps & Data Engineering: Feast (Feature Store), MLflow, Databricks (PySpark), PyDeequ, Airflow/Prefect/Dagster/N8N, Docker, CI/CD (GitHub Actions), ETL/ELT (Requests, Selenium, APIs), Data Governance/Auditing/Validation (Evidently, Great Expectations style)
• LLMs & Generative AI: LangChain, LlamaIndex, CrewAI (agents), OpenAI/Gemini/Claude, RAG pipelines, fine-tuning, vector DBs (MongoDB/Neo4j), prompt engineering, LLMOps (RAGAS, Langfuse)
• Computer Vision & Advanced ML: OpenCV, Reinforcement Learning (Gymnasium), Time Series (LSTM), CNNs/RNNs, Clustering/Classification/Regression
• Data Viz & BI: Power BI, Matplotlib, Seaborn, Plotly, Streamlit, GeoPandas, MS Excel, Google Sheets
• Cloud & Databases: Azure, AWS (SageMaker, etc.), Google Cloud (BigQuery), Heroku, MySQL, PostgreSQL, MongoDB, Neo4j
• Other: Jupyter/VS Code/Anaconda, CUDA (GPU), Linux, Git/GitHub, Bitrix/CRM/Trello, Agile/Scrum, Web Scraping, Data Integration

Check out my full portfolio below — 115+ public repos!

• School Lag Predictor – Master's Thesis — Predictive model for educational delay/lag
• LSTM Stock Price Prediction – Time Series Project — End-to-end forecasting pipeline
• Tech Challenge ML Engineer – Full Multi-Phase Portfolio — Complete end-to-end ML engineering case
• Tech Challenge Phase 3 — Advanced implementation stage
• Deep Learning Coursework — Neural network experiments
• Machine Learning Engineer Coursework — Core MLE topics & assignments

• Feature Store Crash Course – Feast Hands-On (Fraud Detection) — Skew, leakage, latency, duplication, governance
• CD4ML – Continuous Delivery for ML Workshop — Continuous Intelligence & CD4ML practices
• MLOps – ML & APIs — Early MLOps exploration

• Godot + RAG Project — LLM-powered features in Godot engine
• LLM Fine-Tuning Experiments — Fine-tuning workflows
• LlamaIndex + CrewAI Multi-Agent System — Agent orchestration
• LlamaIndex Vector DB RAG Pipeline — Standard RAG implementation
• LlamaIndex Introduction — Getting started with LlamaIndex
• LangChain LLM Development — LangChain-based apps

• Lunar Lander v3 – Reinforcement Learning — Policy gradients / DQN-style solving

• Databricks Notebooks & Pipelines Collection — Modern Databricks workflows
• Databricks Azure Pipeline (Scala) — Azure-integrated pipeline
• Databricks MLlib Recommendation Pipeline — Collaborative filtering
• PyDeequ – Data Quality Checks — Great Expectations-style validation
• Data Profiler & Privacy Techniques — Profiling + anonymization
• Metadata Extraction in Python — File/ data metadata handling
• Various ETL Pipelines — MongoDB, MySQL, API ingestion
• API Requests Pipeline — Web-to-storage flows
• Basic Data Pipelines — Foundational ETL
• Databricks Intro & File Types — Onboarding + formats
• Databricks Data Analysis — Exploratory analysis
• AWS Data Pipeline Exploration — AWS basics

• Motion Detection – OpenCV — Real-time motion tracker
• License Plate OCR — Text detection on plates
• OpenCV OCR Basics — Text extraction
• Face Analysis & Classification – OpenCV — Face detection/recognition
• Hand Tracking — Gesture/hand detection
• Twitter Image Recognition – Computer Vision API — API-based image labeling

• PyTorch – Zero to Mastery Course — Full deep learning with PyTorch
• Scikit-Learn – Regression, Classification, Clustering — Core algorithms
• Advanced Classification & ML Types — Multi-label, imbalanced, etc.
• Recommendation Systems Intro — Collaborative & content-based
• Multilabel Text Classification – NLP — Multi-context NLP
• Decision Trees Deep Dive — Tree-based models
• Credit Scoring Model — Risk modeling
• Hyperparameter Optimization — Grid/random search
• Model Validation Techniques — Cross-validation, metrics
• Clustering – k-means, DBSCAN, Mean Shift — Unsupervised basics
• High-Dimensional Data Handling — Dimensionality reduction
• Statistics with Python – Full Series — Probability, hypothesis testing, correlation
• Pandas Advanced & IO — Data wrangling mastery
• Data Visualization – Matplotlib & Seaborn — Plotting essentials
• GeoPandas – Geospatial Data — Maps & spatial analysis
• Time Series – COVID-19 Analysis — Forecasting basics
• Word2Vec & Embeddings — NLP embeddings
• NLP Intro – Regex & Sentiment — Text basics
• Convolutional & Recurrent Networks – PyTorch — CNNs & RNNs
• And many more foundational notebooks (1–87 series, Alura/Alura-like courses)...

• Streamlit Course Projects — Interactive dashboards
• ChatGPT / OpenAI API Experiments — Early LLM integrations
• Web Scraping with Python — Data collection
• Plotly 3D Globe Map — Interactive geo viz
• MetaTrader 5 – Python Integration — Trading analysis
• JSON to CSV Converter — Data format utils
• Bitrix24 CRM Download Script — API extraction

(Profile config, tests, early experiments, niche or non-technical repos)

• GitHub Profile Config
• Medium Repo Test
• Olist Dataset Exploration
• Financial Markets Analysis
• Iceland Renewable Energy
• Renewable Energy General
• Heritage Foundation Data
• Italian Words List
• Tankard Lyrics Beer Theme
• Python Dashboards (private-turned-public?)
• ...and remaining course/experiment repos not listed above (many 1–87 numbered ones fit fundamentals)

• Medium: https://igorcomune.medium.com/
• LinkedIn: https://www.linkedin.com/in/igor-comune/

Last updated 12/feb/2026