I study artificial intelligence and learn by building complete projects: from raw data to a model trained, evaluated without cheating and served behind an API. All of it with the code public.
Selected projects
Credit Risk Platform
ROC-AUC 0.78 · cost-based threshold 0.14
End-to-end MLOps platform for credit-default scoring: reproducible training with a versioned model registry, a FastAPI inference API with input validation, PSI drift monitoring and a Prometheus and Grafana observability stack, all containerised with CI. Code at github.com/delcenjo/credit-risk-platform.
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Transformer from scratch
Perplexity 6.89 · ablation study
A GPT-style language model implemented from scratch in PyTorch: multi-head attention, causal masking and residual blocks written by hand, plus a byte-pair tokenizer and an ablation study. Code at github.com/delcenjo/transformer-from-scratch.
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This very site
1.7 MB worker · 142 pages · in-browser lab
This portfolio is hand-built with Next.js and deployed on Cloudflare Workers, with a model lab that runs entirely in the browser and continuous deployment on every commit. Code at github.com/jmweb-org/jmweb-pag-web.
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Credit Risk Model
Test ROC-AUC 0.779 · 30,000 clients
Credit-default risk model with rigorous evaluation: a leakage-free pipeline, cross-validated model comparison, calibrated probabilities, a cost-based decision threshold and per-segment error analysis. Code at github.com/delcenjo/credit-risk.
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AI Insight Assistant
Router: RAG + SQL agent behind one box
An assistant that combines RAG and a SQL agent: it retrieves from a document corpus and queries a database, served with FastAPI and Streamlit and packaged in Docker. Code at github.com/delcenjo/ai-insight-assistant.
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RAG Document Assistant
Recall@4 0.88 · live demo on HF Spaces
A retrieval-augmented generation assistant over a document corpus: indexing, semantic retrieval and answers with source citations. Code at github.com/delcenjo/rag-document-assistant.
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LLM SQL Agent
Read-only, with a trace · live demo
An LLM agent that answers questions over a SQL database: it translates natural language into queries, runs them read-only and summarises the result. Code at github.com/delcenjo/llm-sql-agent.
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LLM Eval Harness
Baseline 0.840 acc · macro-F1 0.853
An evaluation harness that compares an LLM against baseline classifiers on a concrete task: reproducible metrics to tell when the LLM is worth it and when it is not. Code at github.com/delcenjo/llm-eval-harness.
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Text Summarizer
700-word chunking · live demo
An abstractive text summariser with chunking for long documents: package, CLI and live demo. Code at github.com/delcenjo/text-summarizer.
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Customer Churn Prediction
CV ROC-AUC 0.845 · the simple model won
End-to-end ML pipeline to predict telecom customer churn: data preparation, modelling and evaluation. Code at github.com/delcenjo/customer-churn-prediction.
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View all projectsBuilt with
The tools, running
$ Real commands, real output, from my open-source suite on PyPI.
Machine learning you can touch
Real models running in your browser. No videos, no tricks.
The network that learns in front of you
Draw points and watch it find the boundary.
Open experimentDraw a digit and it recognises it
A model trained on 30,000 digits, running right here.
Open experimentInside a transformer's attention
Real weights from the transformer I wrote from scratch.
Open experimentCurious?
Creative technologist Building small universes with code *
From raw data to a deployed model
Machine Learning Pipelines
From raw data to a trained model: data preparation, leakage-free validation, model comparison and honest evaluation. Reproducible and tested, not a loose notebook.
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LLM Applications
RAG over documents, agents that query data and applications built on language models, with the evaluation needed to know whether they actually work.
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Deployment & MLOps
Getting the model out of the notebook: an inference API, containers, CI/CD, version registry and drift monitoring. So it does not stay a demo.
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Machine Learning
Deep Learning
MLOps
NLP & LLMs
RAG & embeddings
Model serving
Tabular data
Python
Every project starts from a simple idea: you do not understand a model until you train, evaluate and serve it yourself.
I am José, an artificial intelligence and machine learning student in Madrid. I learn by building projects end to end: data pipelines, ML models, LLM applications and the deployment so nothing stays in the notebook.
I try to make rigour weigh more than the demo: leakage-free validation, honest evaluation, calibrated probabilities and decisions tied to a real cost, not a pretty metric.
Right now: end-to-end projects across classic ML, RAG and agents, a suite of open-source tools published on PyPI, and contributions at Cylstat. Still learning every day.
RAG: verifiable answers over your documents
How retrieval-augmented generation works: indexing, semantic retrieval, chunking and evaluation. Why every answer should be able to cite where it comes from …
