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PODCAST

Vanishing Gradients

Hugo Bowne-Anderson

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

technology

All Episodes

1:20:07

Episode 22: LLMs, OpenAI, and the Existential Crisis...

Vanishing Gradients ·

2023/11/27

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. Jeremy Howard (https://twitter.com/jeremyphoward) is co-founder of fast.ai, an ex-Chief Scientist at Kaggle, and creator of the ULMFiT approach on which all modern language models are based. Shreya Shankar (https://twitter.com/sh_reya) is at UC Berkeley, ex Google

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1:08:11

Episode 21: Deploying LLMs in Production: Lessons...

Vanishing Gradients ·

2023/11/14

Hugo speaks with Hamel Husain, a machine learning engineer who loves building machine learning infrastructure and tools 👷. Hamel leads and contributes to many popular open-source machine learning projects. He also has extensive experience (20+ years) as a machine learning engineer across various industries, including large tech companies like Airbnb and GitHub. At GitHub, he led CodeSearchNet (https://github.com/github/CodeSearchNet), a large language model for semantic search that was a precursor

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1:26:39

Episode 20: Data Science: Past, Present, and Future

Vanishing Gradients ·

2023/10/05

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. Chris is an associate professor of applied mathematics at Columbia University and the New York Times’ chief data scientist, and Matthew is a professor of history at Princeton University and former Guggenheim Fellow. From facial recognition to automated decision systems that inform who gets loans and who

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1:23:19

Episode 19: Privacy and Security in Data Science and...

Vanishing Gradients ·

2023/08/14

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. Previously, she has held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, privacy, and security. In this episode, Hugo and Katharine talk about What

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1:12:42

Episode 18: Research Data Science in Biotech

Vanishing Gradients ·

2023/05/24

Hugo speaks with Eric Ma about Research Data Science in Biotech. Eric leads the Research team in the Data Science and Artificial Intelligence group at Moderna Therapeutics. Prior to that, he was part of a special ops data science team at the Novartis Institutes for Biomedical Research's Informatics department. In this episode, Hugo and Eric talk about What tools and techniques they use for drug discovery (such as mRNA vaccines and medicines); The importance of machine learning, deep learning,

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1:16:04

Episode 17: End-to-End Data Science

Vanishing Gradients ·

2023/02/17

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, including scoping out projects, figuring out the correct questions to ask, how projects can change, delivering on the promise, the importance of rapid prototyping, what it means to put models in production, and how to measure success. And much more, all the while grounding

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1:23:15

Episode 16: Data Science and Decision Making Under...

Vanishing Gradients ·

2022/12/14

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. This is part 2 of a two part conversation in which we delve into decision making under uncertainty. Feel free to check out part 1 here (https://vanishinggradients.fireside.fm/15)

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53:30

Episode 15: Uncertainty, Risk, and Simulation in Data...

Vanishing Gradients ·

2022/12/07

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. This is part 1 of a two part conversation. In this, part 1, we discuss risk, uncertainty, probabilistic thinking, and simulation, all with a view towards improving decision making

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1:09:01

Episode 14: Decision Science, MLOps, and Machine...

Vanishing Gradients ·

2022/11/20

Hugo Bowne-Anderson, host of Vanishing Gradients, reads 3 audio essays about decision science, MLOps, and what happens when machine learning models are everywhere. Links Our upcoming Vanishing Gradients live recording of Data Science and Decision Making Under Uncertainty with Hugo and JD Long! (https://www.eventbrite.com/e/data-science-and-decision-making-under-uncertainty-tickets-467379864757?aff=vg) Decision-Making in a Time of Crisis

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1:22:41

Episode 13: The Data Science Skills Gap, Economics,...

Vanishing Gradients ·

2022/10/11

Hugo speak with Norma Padron 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. Norma is CEO of EmpiricaLab, where her team‘s mission is to bridge work and training and empower healthcare teams to focus on what they care about the most: patient care. In a word, EmpiricaLab is a platform focused on peer learning and last-mile training for healthcare

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1:32:45

Episode 12: Data Science for Social Media: Twitter...

Vanishing Gradients ·

2022/09/30

Hugo speakswith Katie Bauer (https://twitter.com/imightbemary) 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. She’s now Head of Data Science at Gloss Genius so congrats on the new job, Katie! In this conversation, we dive into what type of challenges social media companies face that data science is equipped to solve: in doing so, we traverse the

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1:45:38

Episode 11: Data Science: The Great Stagnation

Vanishing Gradients ·

2022/09/16

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. Mark first came on our radar with an essay he wrote called Machine Learning: the Great Stagnation (https://marksaroufim.substack.com/p/machine-learning-the-great-stagnation), which was concerned with the stagnation in machine learning in academic research and in which he stated Machine learning

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1:26:33

Episode 10: Investing in Machine Learning

Vanishing Gradients ·

2022/08/18

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. Sarah invests in the tools that we both wish we had earlier in our careers: tools that enable data scientists and machine learners to collect, store, manage, analyze, and model data more effectively. As you’ll discover, Sarah identifies as a scientist first and an investor second and still believes that her mission is to

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1:41:42

9: AutoML, Literate Programming, and Data Tooling...

Vanishing Gradients ·

2022/07/19

Hugo speaks with Hamel Husain, Head of Data Science at Outerbounds, with extensive experience in data science consulting, at DataRobot, Airbnb, and Github. In this conversation, they talk about Hamel's early days in data science, consulting for a wide array of companies, such as Crocs, restaurants, and casinos in Las Vegas, diving into what data science even looked like in 2005 and how you could think about delivering business value using data and analytics back then. They talk about his trajectory

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1:05:57

Episode 8: The Open Source Cybernetic Revolution

Vanishing Gradients ·

2022/05/16

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. 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

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1:02:31

Episode 7: The Evolution of Python for Data Science

Vanishing Gradients ·

2022/05/01

Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS. They also talk about the emergence of online collaborative environments, particularly with respect to open source, and attempt to figure out the movings parts of PyData and why it has had the impact it has, including the

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1:27:01

Episode 6: Bullshit Jobs in Data Science (and what to...

Vanishing Gradients ·

2022/04/04

Hugo speaks with Jacqueline Nolis, Chief Product Officer at Saturn Cloud (formerly Head of Data Science), about all types of failure modes in data science, ML, and AI, and they delve into bullshit jobs in data science (yes, that’s a technical term, as you’ll find out) –they discuss the elements that are bullshit, the elements that aren’t, and how to increase the ratio of the latter to the former. They also talk about her journey in moving from mainly working in prescriptive analytics building

en-us

1:48:14

Episode 5: Executive Data Science

Vanishing Gradients ·

2022/03/23

Hugo speaks with Jim Savage, the Director of Data Science at Schmidt Futures, about the need for data science in executive training and decision, what data scientists can learn from economists, the perils of "data for good", and why you should always be integrating your loss function over your posterior. Jim and Hugo talk about what data science is and isn’t capable of, what can actually deliver value, and what people really enjoy doing: the intersection in this Venn diagram is where we need to

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1:44:10

Episode 4: Machine Learning at T-Mobile

Vanishing Gradients ·

2022/03/09

Hugo speaks with Heather Nolis, Principal Machine Learning engineer at T-mobile, about what data science, machine learning, and AI look like at T-mobile, along with Heather’s path from a software development intern there to principal ML engineer running a team of 15. They talk about: how to build a DS culture from scratch and what executive-level support looks like, as well as how to demonstrate machine learning value early on from a shark tank style pitch night to the initial investment through to

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1:32:33

Episode 3: Language Tech For All

Vanishing Gradients ·

2022/03/01

Rachael Tatman is a senior developer advocate for Rasa, where she’s helping developers build and deploy ML chatbots using their open source framework. Rachael has a PhD in Linguistics from the University of Washington where her research was on computational sociolinguistics, or how our social identity affects the way we use language in computational contexts. Previously she was a data scientist at Kaggle and she’s still a Kaggle Grandmaster. In this conversation, Rachael and I talk about the history

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