The Data Exchange Podcast: Alejandro Saucedo on explainability, MLOps, adversarial robustness, and privacy.
In this episode of the Data Exchange I speak with Alejandro Saucedo, Engineering Director at Seldon, a startup building tools for productionizing machine learning. Alejandro is also Chief Scientist at The Institute for Ethical AI & Machine Learning, a UK-based research center that conducts “research into processes and frameworks that support the responsible development, deployment and operation of machine learning systems”.
Our conversation covered Alejandro’s work at both Seldon and the Institute for Ethical AI & Machine Learning:
- We discussed topic areas that the Institute focuses on including explainability, MLOps, adversarial robustness, and privacy-preserving machine learning
- We covered some of the recent output from the Institute including the machine learning maturity model, their open source explainable AI library, their AI-RFX Procurement Framework, and their list of Principles for Responsible AI
- We also discussed his role at Seldon, and areas that Seldon has been focused on.
Our goal in this podcast is to build a community of people interested in Data, Machine Learning and AI. If you have suggestions for us on what to recommend (books, conferences, links), and guests to book, please visit TheDataExchange.media site and fill out the “contact” form.
- Dean Wampler: “Scalable Machine Learning, Scalable Python, For Everyone”
- Morten Dahl: “The state of privacy-preserving machine learning”
- Rajat Monga: “The evolution of TensorFlow and of machine learning infrastructure”
- Reza Zadeh on “Building large-scale, real-time computer vision applications”
- Dafna Shahaf: “Computational humanness, analogy and innovation, and soft concepts”
- “Key AI and Data Trends for 2020”
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[Image: “Tower of London Poppies 1” by Dean Wampler; used with permission.]