I am a Data Developer / AI Operations Engineer recently more focused on Computer Vision.

Work Github: https://github.com/daniel-andrade-st

🔭 Now: at Sporttotal's Infra Team, as a Computer Vision Operations Engineer.

From data pipelines to production environments, my journey has been about laying the groundwork for Data and AI projects, then evolving them into scalable, productized services. Currently, I’ve taken on the challenge of helping directly in the integration of AI into real-world applications with good software engineering practices.

As AI projects mature, with the combination of MLOps for Computer Vision (or CVOps, if you prefer) practices and infra expertise, our goal is to bridge the gap between innovative AI models and the production environments where they deliver real value.

I’m now more focused (my growing interest in how computers and memory work had a part on this) on creating robust systems that make AI a seamless part of modern products, bringing ideas from POC to production, ensuring they scale efficiently.

📆 Before, at Sporttotal's Data & AI Team.

After starting as a Data Somethingist, where I had to solve (from the 3rd to 1st world) data problems (from structured data to CV, from reporting to scraping, from pipelining to research), I took the role of DataOps Eng. for Computer Vision - which required more CV skills and a direct relationship with video processing and data management. While helping to set a full ecosystem through good Data & ML practices.

🗂️ Before Sporttotal.

At TicAPP, where the main goal was to make the Public Administration data-driven, I built data products, pipelines and APIs.

Having dealt with public services data, worked on simulations & optimization, visualization and NLP, having also to engineer processes with open data to make it accessible through APIs.

Main previous missions:

• Developing AI/Deep Learning algorithms in Computer Vision for an app in the insurance sector.

• Leader of the Data Science Lab@BUPi with the aim of helping the Portuguese government to have a better understanding of the territory in the scope of the ambitious BUPi project (http://bupi.gov.pt), making a social and environmental impact.

The goal was to develop a model of predicted spatial localization of properties from text and sparse data, along with geocoordinate information, to complement the results obtained from remote sensing, in order to enhance georeferencing actions.

Prize winner for Best Digital Transformation Project in the Portugal Digital Awards 2019: https://cofinaeventos.com/portugaldigitalawards/vencedores-2019. See TV mention (in PT): https://sicnoticias.pt/programas/futurohoje/2019-07-30-Tem-o-seu-terreno-registado--Um-metodo-mais-simples-esta-a-chegar

Previously, projects in NLP/text analytics and in Computer Vision.