Vanishing Gradients
a data podcast with hugo bowne-anderson
About the show
A podcast about all things data, brought to you by data scientist Hugo Bowne-Anderson.
It's time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We'll dive deep into all the moving parts of the data world, so if you're new to the space, you'll have an opportunity to learn from the experts. And if you've been around for a while, you'll find out what's happening in many other parts of the data world.
Episodes
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Episode 23: Statistical and Algorithmic Thinking in the AI Age
December 21st, 2023 | Season 1 | 1 hr 20 mins
Hugo speaks with Allen Downey, curriculum designer at Brilliant, Professor Emeritus at Olin College, and author, about the key statistical and data skills we all need to navigate an increasingly data-driven and algorithmic world. The goal will be to dive deep into the statistical paradoxes and fallacies that get in the way of using data to make informed decisions.
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Episode 22: LLMs, OpenAI, and the Existential Crisis for Machine Learning Engineering
November 28th, 2023 | Season 1 | 1 hr 20 mins
Jeremy Howard (Fast.ai), Shreya Shankar (UC Berkeley), and Hamel Husain (Parlance Labs) join Hugo Bowne-Anderson to talk about how LLMs and OpenAI are changing the worlds of data science, machine learning, and machine learning engineering.
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Episode 21: Deploying LLMs in Production: Lessons Learned
November 14th, 2023 | Season 1 | 1 hr 8 mins
Hugo speaks with Hamel Husain (ex-Github, Airbnb), a machine learning engineer who loves building machine learning infrastructure and tools, about generative AI, large language models, the business value they can generate, and how to get started.
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Episode 20: Data Science: Past, Present, and Future
October 5th, 2023 | Season 1 | 1 hr 26 mins
Hugo speaks with Chris Wiggins (Columbia, NYTimes) and Matthew Jones (Princeton) about their recent book How Data Happened, and the Columbia course it expands upon, data: past, present, and future.
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Episode 19: Privacy and Security in Data Science and Machine Learning
August 15th, 2023 | Season 1 | 1 hr 23 mins
Hugo speaks with Katharine Jarmul about privacy and security in data science and machine learning. Katharine is a Principal Data Scientist at Thoughtworks Germany focusing on privacy, ethics, and security for data science workflows.
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Episode 18: Research Data Science in Biotech
May 25th, 2023 | Season 1 | 1 hr 12 mins
ai, biotech, data science, machine learning, open source, python
Machine learning, deep learning, Bayesian inference for drug discovery, OSS, and accelerating discovery science to the speed of thought!
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Episode 17: End-to-End Data Science
February 17th, 2023 | Season 1 | 1 hr 16 mins
It’s time to get real about how data science and machine learning actually deliver value! Hugo speaks with Tanya Cashorali, a data scientist and consultant that helps businesses get the most out of data, about what end-to-end data science looks like across many industries, such as retail, defense, biotech, and sports.
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Episode 16: Data Science and Decision Making Under Uncertainty
December 15th, 2022 | Season 1 | 1 hr 23 mins
Hugo speaks with JD Long, agricultural economist, quant, and stochastic modeler, about data science, ML, and the nitty gritty of decision making under uncertainty, including how we can use our knowledge of risk, uncertainty, probabilistic thinking, causal inference, and more to help us use data science and machine learning to make better decisions in an uncertain world.
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Episode 15: Uncertainty, Risk, and Simulation in Data Science
December 8th, 2022 | Season 1 | 53 mins 30 secs
Hugo speaks with JD Long, agricultural economist, quant, and stochastic modeler, about decision making under uncertainty and how we can use our knowledge of risk, uncertainty, probabilistic thinking, causal inference, and more to help us use data science and machine learning to make better decisions in an uncertain world.
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Episode 14: Decision Science, MLOps, and Machine Learning Everywhere
November 21st, 2022 | Season 1 | 1 hr 9 mins
Hugo reads 3 audio essays about decision science, MLOps, and what happens when machine learning models are everywhere
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Episode 13: The Data Science Skills Gap, Economics, and Public Health
October 12th, 2022 | Season 1 | 1 hr 22 mins
Hugo speaks with Norma Padron, CEO of EmpiricaLab, about data science education and continuous learning for people working in healthcare, broadly construed, along with how we can think about the democratization of data science skills more generally.
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Episode 12: Data Science for Social Media: Twitter and Reddit
September 30th, 2022 | Season 1 | 1 hr 32 mins
Hugo speaks with Katie Bauer about her time working in data science at both Twitter and Reddit. At the time of recording, Katie was a data science manager at Twitter and prior to that, a founding member of the data team at Reddit.
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Episode 11: Data Science: The Great Stagnation
September 16th, 2022 | Season 1 | 1 hr 45 mins
Hugo speaks with Mark Saroufim, an Applied AI Engineer at Meta who works on PyTorch where his team’s main focus is making it as easy as possible for people to deploy PyTorch in production outside Meta.
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Episode 10: Investing in Machine Learning
August 19th, 2022 | Season 1 | 1 hr 26 mins
Hugo speaks with Sarah Catanzaro, General Partner at Amplify Partners, about investing in data science and machine learning tooling and where we see progress happening in the space.
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9: AutoML, Literate Programming, and Data Tooling Cargo Cults
July 19th, 2022 | Season 1 | 1 hr 41 mins
Hugo speaks with Hamel Husain, Head of Data Science at Outerbounds, with extensive experience in data science consulting, at DataRobot, Airbnb, and Github.
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Episode 8: The Open Source Cybernetic Revolution
May 16th, 2022 | Season 1 | 1 hr 5 mins
Hugo speaks with Peter Wang, CEO of Anaconda, about what the value proposition of data science actually is, data not as the new oil, but rather data as toxic, nuclear sludge, the fact that data isn’t real (and what we really have are frozen models), and the future promise of data science, Gifting economies with finite game economics thrust onto them.
They also dive into an experimental conversation around open source software development as a model for the development of human civilization, in the context of developing systems that prize local generativity over global extractive principles. If that’s a mouthful, which it was, or an earful, which it may have been, all will be revealed in the conversation.