Pedro Morato Lahoz PedroM2626

🧠 Machine Learning • ⚙️ ML Systems • 🔬 Experimentation

🧠 About Me

🎓 Computer Science student at UniCEUB, focused on Machine Learning, AI Engineering and ML Systems.

I like going beyond isolated notebooks and models. My work revolves around one question:

How can we build, evaluate and understand Machine Learning systems in a reproducible way?

I started programming in 2024 and began focusing heavily on Machine Learning in 2025.

At a glance

🎓 Computer Science @ UniCEUB
🚀 Programming since 2024
🧠 Full ML focus since 2025
⚙️ End-to-end ML systems
🔬 Reproducibility first

My work combines 🔬 Experimentation & Benchmarking · ⚙️ ML Engineering & MLOps · 🧠 Machine & Deep Learning · 🎮 Reinforcement Learning · ☁️ Cloud ML platforms

🚀 Featured Projects

⚙️ AutoMLOps Studio

Educational platform for experimenting with Machine Learning while applying real MLOps practices.

Stack Python MLflow Optuna Docker

Focus Experiment tracking · Automation · HPO · Reproducibility

🔬 Multi-AutoML Interface

Experimental interface for comparing different AutoML frameworks under similar conditions.

Frameworks AutoGluon FLAML TPOT H2O AutoML

Focus Benchmarking · Metrics · Framework comparison

🎮 RL Experiments Lab

Experimental laboratory for Reinforcement Learning and benchmarking.

Stack PPO RLlib SB3 CleanRL ML-Agents

Focus Architecture · Representation · Seeds · Generalization

🔭 More experiments

More repositories covering other areas of ML:

🧠 Supervised Learning · 🎲 Unsupervised Learning
🤖 Reinforcement Learning · 📊 AutoML
⚙️ MLOps · ☁️ Cloud ML platforms

🧪 My ML Approach

➜ ➜ ➜ ➜ ➜

I am particularly interested in experiments where the goal is not simply the highest score, but understanding why different approaches behave differently.

🛠️ Tech Stack

🧠 Machine Learning

PyTorch TensorFlow Scikit-learn Optuna

⚙️ Engineering & MLOps

MLflow DagsHub Hugging Face Docker

🌐 Backend & Interfaces

FastAPI Flask React TypeScript

☁️ Cloud & ML Platforms

Azure ML AWS Google Cloud watsonx

🔬 Areas I'm Exploring

📊 GitHub Analytics

📅 Contribution history

🎯 Current Direction

Machine Learning
       │
       ├── ML Engineering
       │      ├── MLOps
       │      ├── Pipelines
       │      └── ML Systems
       │
       ├── Experimentation
       │      ├── Benchmarking
       │      ├── Model Comparison
       │      └── Reproducibility
       │
       └── Research Exploration
              ├── Reinforcement Learning
              ├── Scientific ML
              ├── Causal ML
              └── Probabilistic ML

My goal is to become a Machine Learning Engineer capable of building, evaluating and understanding complete ML systems.

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