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! Decision-Making in a Time of Crisis by Hugo Bowne-Anderson MLOps and DevOps: Why Data Makes It Different by Ville Tuulos and Hugo Bowne-Anderson The above essay syndicated on VentureBeat When...

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...

Hugo speakswith 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. 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 difference and...

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, which was concerned with the stagnation in machine learning in academic research and in which he stated Machine learning researchers can now engage in risk-free, high-income, high-prestige...

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...

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...

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...

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,...

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 b******t jobs in data science (yes, that’s a technical term, as you’ll find out) –they discuss the elements that are b******t, 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...

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...

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...

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...

Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at platform.ai. In this conversation, we’ll be talking about the history of data science, machine learning, and AI, where we’ve come from and where we’re going, how new...

In this brief introduction, Hugo introduces the rationale behind launching a new data science podcast and gets excited about his upcoming guests: Jeremy Howard, Rachael Tatman, and Heather Nolis! Original music, bleeps, and blops by local Sydney legend PlaneFace!