Meditations on First Deployment: A Practical Guide to Responsible Data Science & Engineering


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As the impact of data science & engineering increasingly reaches farther and wider, our professional responsibility as practitioners becomes more critical to society. In this talk we will cover practical steps that engineers and data scientists can take to ensure best practice, and reduce the probability of undesired outcomes when designing, building and deploying data-driven systems.


Alejandro Saucedo

Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads the development of industry standards on machine learning bias, adversarial attacks and differential privacy. Alejandro is also the Director of Machine Learning Engineering at Seldon Technologies, where he leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has delivered multi-national projects with top tier investment banks, magic circle law-firms and global insurance companies. He has a strong track record building cross-functional departments of software engineers from scratch, and leading the delivery of large-scale machine learning systems across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).

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