Episode 17: End-to-End Data Science

Vanishing Gradients

#Tech #Hugo BowneAnderson Its #Data Science #Machine Learning

34:07

In this insightful episode of The Innovators & Investors Podcast, host Kristian Marquez sits down with Jay Crone, Managing Director and Venture Capitalist at Deloitte Ventures, to explore the firm’s strategic approach to corporate venture capital. Jay shares Deloitte Ventures’ investment thesis, focusing on supporting innovative Canadian startups at Series A and B stages across key sectors like cybersecurity, climate tech, fintech, future of work, health tech, and AI. Listeners will gain an inside look at how Deloitte leverages its vast network of 1,500 partners and 15,000 employees to source deals and add value beyond capital by helping startups navigate Deloitte’s complex ecosystem and access enterprise clients. Jay also discusses his diverse career journey—from government and investment banking to entrepreneurship and corporate VC—and how those experiences shape his investment philosophy. The episode delves into Deloitte’s due diligence process, the importance of founder relationships, and the firm’s strategic role as a co-investor. Jay highlights emerging trends, particularly the promise of vertical AI tailored to industry-specific needs, and shares his bullish outlook on fintech innovations like stablecoins and cross-border payments. He offers candid advice for entrepreneurs on risk-taking and aligning business vision with funding goals. This episode is a must-listen for founders, investors, and anyone interested in the evolving landscape of corporate venture capital and innovation in Canada. Learn more about Jay's work at https://www.deloitte.com/ca/en/services/program/ventures.html Connect with Jay on LinkedIn at https://www.linkedin.com/in/jaycrone/ Think you'd be a great guest on the show? Apply at https://finstratmgmt.com/innovators-investors-podcast/ Want to learn more about Kristian Marquez's work? Check out his website at https://finstratmgmt.com…

2 years ago 1:16:04

MP3•Episode home

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 their conversation in real-world examples from data science, business, and life.

In a world where most organizations think they need AI and yet 10-15% of data science actually involves model building, it’s time to get real about how data science and machine learning actually deliver value!

LINKS

54 episodes

Episode 17: End-to-End Data Science

Vanishing Gradients

25 subscribers

published 2 years ago

MP3•Episode home

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 their conversation in real-world examples from data science, business, and life.

LINKS

54 episodes

#Tech #Hugo BowneAnderson Its #Data Science #Machine Learning

All episodes

3 years ago53:30

53:30

Vanishing Gradients

1
Episode 14: Decision Science, MLOps, and Machine Learning Everywhere 1:09:01

3 years ago1:09:01

1:09:01

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 models are everywhere by Hugo Bowne-Anderson and Mike Loukides…

Vanishing Gradients

1
Episode 13: The Data Science Skills Gap, Economics, and Public Health 1:22:41

3 years ago1:22:41

1:22:41

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 teams. As you’ll discover, Norma’s background is fascinating: with a Ph.D. in health policy and management from Yale University, a master's degree in economics from Duke University (among other things), and then working with multiple early stage digital health companies to accelerate their growth and scale, this is a wide ranging conversation about how and where learning actually occurs, particularly with respect to data science; we talk about how the worlds of economics and econometrics, including causal inference, can be used to make data science and more robust and less fragile field, and why these disciplines are essential to both public and health policy. It was really invigorating to talk about the data skills gaps that exists in organizations and how Norma’s team at Empiricalab is thinking about solving it in the health space using a 3 tiered solution of content creation, a social layer, and an information discovery platform. All of this in service of a key question we’re facing in this field: how do you get the right data skills, tools, and workflows, in the hands of the people who need them, when the space is evolving so quickly? Links Norma's website EmpiricaLab Norma on twitter…

Vanishing Gradients

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Episode 12: Data Science for Social Media: Twitter and Reddit 1:32:45

3 years ago1:32:45

1:32:45

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 similarities in companies such as Twitter and Reddit, the major differences in being an early member of a data team and joining an established data function at a larger organization, the supreme importance of robust measurement and telemetry in data science, along with the mixed incentives for career data scientists, such as building flashy new things instead of maintaining existing infrastructure. I’ve always found conversations with Katie to be a treasure trove of insights into data science and machine learning practice, along with key learnings about data science management. In a word, Katie helps me to understand our space better. In this conversation, she told me that one important function data science can serve in any organization is creating a shared context for lots of different people in the org. We dive deep into what this actually means, how it can play out, traversing the world of dashboards, metric stores, feature stores, machine learning products, the need for top-down support, and much, much more.…

Vanishing Gradients

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Episode 11: Data Science: The Great Stagnation 1:45:38

3 years ago1:45:38

1:45:38

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 work. They are today’s Medieval Catholic priests. This is just the tip of the icebergs of Mark’s critical and often sociological eye and one of the reasons I was excited to speak with him. In this conversation, we talk about the importance of open source software in modern data science and machine learning and how Mark thinks about making it as easy to use as possible. We also talk about risk assessments in considering whether to adopt open source or not, the supreme importance of good documentation, and what we can learn from the world of video game development when thinking about open source. We then dive into the rise of the machine learning cult leader persona, in the context of examples such as Hugging Face and the community they’ve built. We discuss the role of marketing in open source tooling, along with for profit data science and ML tooling, how it can impact you as an end user, and how much of data science can be considered differing forms of live action role playing and simulation. We also talk about developer marketing and content for data professionals and how we see some of the largest names in ML researchers being those that have gigantic Twitter followers, such as Andrei Karpathy. This is part of a broader trend in society about the skills that are required to capture significant mind share these days. If that’s not enough, we jump into how machine learning ideally allows businesses to build sustainable and defensible moats, by which we mean the ability to maintain competitive advantages over competitors to retain market share. In between this interview and its release, PyTorch joined the Linux Foundation, which is something we’ll need to get Mark back to discuss sometime. Links The Myth of Objective Tech Screens Machine Learning: The Great Stagnation Fear the Boom and Bust: Keynes vs. Hayek - The Original Economics Rap Battle! History and the Security of Property by Nick Szabo Mark on YouTube Mark's Substack Mark's Discord…

Vanishing Gradients

1
Episode 10: Investing in Machine Learning 1:26:33

3 years ago1:26:33