Frequently asked questions
Personal projects, built end to end, where data is used to make a decision. Classification and regression models for tabular data (credit risk, churn), NLP with transformers (classification, extraction, semantic search), LLM applications (RAG over documents, SQL agents), and the MLOps side of my own models: inference API, versioning, CI/CD and drift monitoring. Everything is public on GitHub, with the code, the evaluation and the parts that did not work.
Studying. I am a student of artificial intelligence and machine learning, and alongside that I contribute at Cylstat and to open source projects (Spanish translations for HuggingFace and FastAPI, answers in community forums). The portfolio is where I put what I learn into practice: if I read about calibration, I calibrate a real model; if I read about drift, I set up the monitoring myself.
Yes. I am open to internships and first junior roles in data or machine learning, in Madrid or remote. What I can show today: end-to-end projects with public code, a suite of CLI tools published on PyPI with tests and CI, open source contributions, and the willingness to learn from people who know more than I do. Write to me through the contact form and I will reply.
Python as the base. scikit-learn and PyTorch for modelling, pandas for data, FastAPI and Pydantic for serving, Docker and GitHub Actions for reproducibility, Prometheus and Grafana for monitoring. For LLM applications: embeddings, vector stores and evaluation of the pipeline itself. That said, I try not to marry the tools: the problem chooses, not the habit.
Of course. They are published on PyPI and installable with pip, MIT licensed. Each one does one small thing well: checking leakage between train and test, counting tokens and estimating LLM costs, evaluation gates for CI, serving models. If you find a bug or have an idea, open an issue or a pull request. That kind of feedback is exactly why I publish them.
All of it is on GitHub: my personal projects at github.com/delcenjo and the open-source tools at github.com/jmweb-org. Every project on this site links to its repository, and the Open Source section documents each tool. I would rather show code than adjectives: if you want to know how I work, the repositories answer better than I do.
Five phases, no mystery. 1. Data and problem — understand the data, define the target variable and a metric that measures what matters. 2. Pipeline — preprocessing inside the pipeline, leakage-free cross-validation. 3. Modelling and evaluation — model comparison, calibration, cost-based threshold, per-segment error analysis. 4. Deployment — inference API, Docker, version registry, monitoring. 5. Documentation — README with decisions and results, so the project can be rerun without me. It is the method I am teaching myself to follow on every project, precisely because it is easy to skip steps when nobody is checking.
Please do. Feedback on any project or tool is the fastest way for me to improve: an issue on GitHub, a pull request or a message through the form all work. For collaborations, I am up for joining projects where I can contribute and learn — especially anything involving evaluation, data quality or taking models out of the notebook.
Because it forces me to do things better. A private notebook forgives everything; a public repository with tests, CI and a README that says what works and what does not forgives much less. It also lets anyone check what I claim: if this site says a project has drift monitoring, the code showing it is one click away. And honestly, the feedback I get from publishing is the best teacher I have.