Podcast appearances and mentions of hugo bowne anderson

Hugo Bowne-Anderson, Independent Data & AI Scientist, joins us to tackle why most AI applications fail to make it past the demo stage. We'll explore his concept of Evaluation-Driven Development (EDD) and how treating evaluation as a continuous process—not just a final step—can help teams escape "Proof-of-Concept Purgatory." How can we build AI applications that remain reliable and adaptable over time? What shifts are happening as boundaries between data, ML, and product development collapse? From practical testing approaches to monitoring strategies, this episode offers essential insights for anyone looking to create AI applications that deliver genuine business value beyond the initial excitement.

Hugo Bowne-Anderson, Independent Data & AI Scientist, joins us to tackle why most AI applications fail to make it past the demo stage. We'll explore his concept of Evaluation-Driven Development (EDD) and how treating evaluation as a continuous process—not just a final step—can help teams escape "Proof-of-Concept Purgatory." How can we build AI applications that remain reliable and adaptable over time? What shifts are happening as boundaries between data, ML, and product development collapse? From practical testing approaches to monitoring strategies, this episode offers essential insights for anyone looking to create AI applications that deliver genuine business value beyond the initial excitement.

Hugo Bowne-Anderson and I chat about the future of AI and automation, agents, and much more.

Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough questions shaping the field. The panel features: - Ben Taylor (Jepson) (https://www.linkedin.com/in/jepsontaylor/) – CEO and Founder at VEOX Inc., with experience in AI exploration, genetic programming, and deep learning. - Joe Reis (https://www.linkedin.com/in/josephreis/) – Co-founder of Ternary Data and author of Fundamentals of Data Engineering. - Juan Sequeda (https://www.linkedin.com/in/juansequeda/) – Principal Scientist and Head of AI Lab at Data.World, known for his expertise in knowledge graphs and the semantic web. The discussion unpacks essential topics such as: - The shift from prompt engineering to goal engineering—letting AI iterate toward well-defined objectives. - Whether generative AI is having an electricity moment or more of a blockchain trajectory. - The combinatorial power of AI to explore new solutions, drawing parallels to AlphaZero redefining strategy games. - The POC-to-production gap and why AI projects stall. - Failure modes, hallucinations, and governance risks—and how to mitigate them. - The disconnect between executive optimism and employee workload. Hugo also mentions his upcoming workshop on escaping Proof-of-Concept Purgatory, which has evolved into a Maven course "Building LLM Applications for Data Scientists and Software Engineers" launching in January (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor). Vanishing Gradient listeners can get 25% off the course (use the code VG25), with $1,000 in Modal compute credits included. A huge thanks to Dave Scharbach and the Toronto Machine Learning Society for organizing the conference and to the audience for their thoughtful questions. As we head into the new year, this conversation offers a reality check amidst the growing AI agent hype. LINKS Hugo on twitter (https://x.com/hugobowne) Hugo on LinkedIn (https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/) Vanishing Gradients on twitter (https://x.com/vanishingdata) "Building LLM Applications for Data Scientists and Software Engineers" course (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor).

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Effective data science education requires feedback and rapid iteration.Building LLM applications presents unique challenges and opportunities.The software development lifecycle for AI differs from traditional methods.Collaboration between data scientists and software engineers is crucial.Hugo's new course focuses on practical applications of LLMs.Continuous learning is essential in the fast-evolving tech landscape.Engaging learners through practical exercises enhances education.POC purgatory refers to the challenges faced in deploying LLM-powered software.Focusing on first principles can help overcome integration issues in AI.Aspiring data scientists should prioritize problem-solving over specific tools.Engagement with different parts of an organization is crucial for data scientists.Quick paths to value generation can help gain buy-in for data projects.Multimodal models are an exciting trend in AI development.Probabilistic programming has potential for future growth in data science.Continuous learning and curiosity are vital in the evolving field of data science.Chapters:09:13 Hugo's Journey in Data Science and Education14:57 The Appeal of Bayesian Statistics19:36 Learning and Teaching in Data Science24:53 Key Ingredients for Effective Data Science Education28:44 Podcasting Journey and Insights36:10 Building LLM Applications: Course Overview42:08 Navigating the Software Development Lifecycle48:06 Overcoming Proof of Concept Purgatory55:35 Guidance for Aspiring Data Scientists01:03:25 Exciting Trends in Data Science and AI01:10:51 Balancing Multiple Roles in Data Science01:15:23 Envisioning Accessible Data Science for AllThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim

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 brain, Facebook, and Viaduct. Hamel Husain (https://twitter.com/HamelHusain) has his own generative AI and LLM consultancy Parlance Labs (https://parlance-labs.com/) and was previously at Outerbounds, Github, and Airbnb. They talk about How LLMs shift the nature of the work we do in DS and ML, How they change the tools we use, The ways in which they could displace the role of traditional ML (e.g. will we stop using xgboost any time soon?), How to navigate all the new tools and techniques, The trade-offs between open and closed models, Reactions to the recent Open Developer Day and the increasing existential crisis for ML. LINKS The panel on YouTube (https://youtube.com/live/MTJHvgJtynU?feature=share) Hugo and Jeremy's upcoming livestream on what the hell happened recently at OpenAI, among many other things (https://lu.ma/byxyzfrr?utm_source=vg) Vanishing Gradients on YouTube (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) Vanishing Gradients on twitter (https://twitter.com/VanishingData)

We talked about: Hugo's background Why do tools and the companies that run them have wildly different names Hugo's other projects beside Metaflow Transitioning from educator to DevRel What is DevRel? DevRel vs Marketing How DevRel coordinates with developers How DevRel coordinates with marketers What skills a DevRel needs The challenges that come with being an educator Becoming a good writer: nature vs nurture Hugo's approach to writing and suggestions Establishing a goal for your content Choosing a form of media for your content Is DevRel intercompany or intracompany? The Vanishing Gradients podcast Finding Hugo online Links: Hugo Browne's github: http://hugobowne.github.io/ Vanishing Gradients: https://vanishinggradients.fireside.fm/ MLOps and DevOps: Why Data Makes It Differenthttps://www.oreilly.com/radar/mlops-and-devops-why-data-makes-it-different/ Evaluate Metaflow for free, right from your Browser: https://outerbounds.com/sandbox/ Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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 (https://www.oreilly.com/radar/decision-making-in-a-time-of-crisis/) by Hugo Bowne-Anderson MLOps and DevOps: Why Data Makes It Different (https://www.oreilly.com/radar/mlops-and-devops-why-data-makes-it-different/) by Ville Tuulos and Hugo Bowne-Anderson The above essay syndicated on VentureBeat (https://venturebeat.com/business/mlops-vs-devops-why-data-makes-it-different/) When models are everywhere (https://www.oreilly.com/radar/when-models-are-everywhere/) by Hugo Bowne-Anderson and Mike Loukides

This week I spoke with Savin Goyal and Hugo Bowne-Anderson from Outerbounds. They both work on leading, building and helping people put models into production through Metaflow, and I'm sure current users of ZenML will find this conversation interesting to hear how they think through the broader questions and engineering problems involved with MLOps. Above all, we spoke about the challenges involved in building a tool that handles the whole machine learning story, from collecting data to training models, to deployment and back again. In many ways it's great that there are lots of smart people thinking about this really hard problem, and even though it is by no means 'solved' conversations like this make me feel cautiously optimistic about the space. Special Guests: Hugo Bowne-Anderson and Savin Goyal.

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 focus energy and it may not be quite what you think it is! They then dive into Jim's thoughts on what he dubs Executive Data Science. You may be aware of the slicing of the data science and machine learning spaces into descriptive analytics, predictive analytics, and prescriptive analytics but, being the thought surgeon that he is, Jim proposes a different slicing into (1) tool building OR data science as a product, (2) tools to automate and augment parts of us, and (3) what Jim calls Executive Data Science. Jim and Hugo also talk about decision theory, the woeful state of causal inference techniques in contemporary data science, and what techniques it would behoove us all to import from econometrics and economics, more generally. If that's not enough, they talk about the importance of thinking through the data generating process and things that can go wrong if you don't. In terms of allowing your data work to inform your decision making, thery also discuss Jim's maxim “ALWAYS BE INTEGRATING YOUR LOSS FUNCTION OVER YOUR POSTERIOR” Last but definitively not least, as Jim has worked in the data for good space for much of his career, they talk about what this actually means, with particular reference to fast.ai founder & QUT professor of practice Rachel Thomas' blog post called “Doing Data Science for Social Good, Responsibly” (https://www.fast.ai/2021/11/23/data-for-good/). Rachel's post takes as its starting point the following words of Sarah Hooker, a researcher at Google Brain: "Data for good" is an imprecise term that says little about who we serve, the tools used, or the goals. Being more precise can help us be more accountable & have a greater positive impact. And Jim and I discuss his work in the light of these foundational considerations. Links Jim on twitter (https://twitter.com/abiylfoyp/) What Is Causal Inference?An Introduction for Data Scientists (https://www.oreilly.com/radar/what-is-causal-inference/) by Hugo Bowne-Anderson and Mike Loukides Jim's must-watch Data Council talk on Productizing Structural Models (https://www.datacouncil.ai/talks/productizing-structural-models)

I always enjoy a look into the lives and activities of those fortunate professionals who carry the title of 'Evangelist.' Hugo Bowne-Anderson, Head of Data Science Evangelism and Marketing for Coiled joined me from the future, Sydney, Australia for a conversation on all things Coiled, Dask, and the Pythonic ecosystem. Hugo shares a unique perspective, having been a practicing data scientist in the world of molecular cell biology, as well as an instructor, developing and delivering online interactive data sciences classes that have been enjoyed by over half a million learners. A big part of Hugo's mission is the capture and share the stories of how people are using data science to improve our lives, one application at a time. Thanks, Hugo. Show Notes LinkedIn Article YouTube Video (DISCLOSURE*: This interview was sponsored by Coiled. Neither Coiled nor other sponsors have editorial control over the content)

[Part 1 of 2] Listeners join in for a wonderful conversation in this episode. Our guests Matthew Rocklin and Hugo Bowne-Anderson are extending access to powerful distributed computing for more data users with their startup Coiled (https://coiled.io/). Data scientists with a two minute download of Coiled’s software (https://cloud.coiled.io/) can scale their work to the cloud. We discuss during the episode how conversations with the open source community resembles early customer conversations commonly used by entrepreneurs in a lean startup framework. Dask’s creator and Coiled founder Matthew described his software design approach that has a decided minimalist bent. A great benefit for users of popular Python libraries because of Matt’s approach is a familiar interface when using Dask or Coiled to extend the power of popular PyData stack tools. Our conversation turns to how Coiled has the capability to extend more computation power to many casual users of Python who are interested in solving data problems pragmatically without rebuilding a data factory every time. [Join us next week for Part 2]

Dask What is it? Parallelism for analytics What is parallelism? Doing a lot at once by splitting tasks into smaller subtasks which can be processed in parallel (at the same time) Distributed work across multiple machines and then combining the results Helpful for CPU bound - doing a bunch of calculations on the CPU. The rate at which process progresses is limited by the speed of the CPU Concurrency? Similar but a but things don’t have to happen at the same time, they can happen asynchronously. They can overlap. Shared state Helpful to I/O bound - networking, reading from disk, etc. The rate at which a process progresses is limited by the speed of the I/O subsystem. Multi-core vs distributed Multi-core is a single processor with 2 or more cores that can cooperate through threads - multithreading Distributed is across multiple nodes communicating via HTTP or RPC Why is this hard? Python has it challenges due to GIL, other languages don't have this problem Shared state can lead to potential race conditions, deadlocks, etc Coordination work across the machines For analytics? Calculating some statistics on a large dataset can be tricky if it can’t fit in memory // Show Notes Coiled Cloud: https://cloud.coiled.io/ Coiled Launch Announcement: https://medium.com/coiled-hq/coiled-dask-for-everyone-everywhere-376f5de0eff4 OSS article: https://www.forbes.com/sites/glennsolomon/2020/09/15/monetizing-open-source-business-models-that-generate-billions/#2862e47234fd Amish barn raising: https://www.youtube.com/watch?v=y1CPO4R8o5M MessagePassingInterface: https://en.wikipedia.org/wiki/Message_Passing_Interface ----------- Connect With Us ✌️------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Matthew on LinkedIn: https://www.linkedin.com/in/matthew-rocklin-461b4323/ Timestamps: 0:00 - Intro to Matthew Rocklin and Hugo Bowne-Anderson 0:37 - Matthew Rocklin's Background 1:17 - Hugo Brown-Anderson's Background 3:47 - Where did that inspiration come from? 10:04 - Is there a close relationship between Best Practices and Tooling or are these two separate things? 11:27 - Why is Data Literacy important with Coiled? 14:46 - How do you think about the balance between enabling Data Science to have a lot of powerful compute? 17:05 - Machine Learning as a space for tracking best practices experimentation 19:32 - What makes Data Science so difficult? 24:07 - How can a for-profit company compliment Open Source Software (OSS) 29:40 - Amazon becoming a competitor with your own open-source technology (?) 32:50 - How do you encourage more people to contribute and ensure quality? 34:58 - Do you see Coiled operating within the DASK ecosystem? 37:30 - What is DASK? 39:19 - What should people know about parallelism? 41:28 - Why is it so hard to put things back together? 41:34 - Why does Python need a whole new tool to enable that? Or maybe some other tools as well? 44:44 - Dynamic Tasks Scheduling as being useful to Data Scientists 47:15 - Why is reliability in particular important in Data Science? 52:27 - What's in store for DASK?

If you're into data science, you've probably heard about Dask. It's a package that feels like familiar APIs such as Numpy, Pandas, and Scikit-Learn. Yet it can scale that computation across CPU cores on your local machine all the way to distributed grid-based computing in large clusters. While powerful, this may take some serious setup to execute in its full glory. That's why Matthew Rocklin has teamed up with Hugo Bowne-Anderson and others to launch a business to help Python loving data scientists run Dask workloads in the cloud. And they are here to tell us about they open-source foundation business. And they must be on to something, between recording and releasing this episode, they raised $5M in VC funding. Links from the show Hugo on Twitter: @hugobowne Matthew on Twitter: @mrocklin Coiled: coiled.io Coiled raised $5M in Sept: twitter.com A brief history of dask article: coiled.io/blog Coiled: Dask for Everyone, Everywhere: medium.com The incredible growth of python: stackoverflow.blog Growth updated (SO Trends current): insights.stackoverflow.com Coiled Youtube channel: youtube.com Snorkel package: pypi.org Sponsors Brilliant Monday.com Talk Python Training

Python is a leading choice for data science due to the immense number of libraries and frameworks readily available to support it, but it is still difficult to scale. Dask is a framework designed to transparently run your data analysis across multiple CPU cores and multiple servers. Using Dask lifts a limitation for scaling your analytical workloads, but brings with it the complexity of server administration, deployment, and security. In this episode Matthew Rocklin and Hugo Bowne-Anderson discuss their recently formed company Coiled and how they are working to make use and maintenance of Dask in production. The share the goals for the business, their approach to building a profitable company based on open source, and the difficulties they face while growing a new team during a global pandemic.

Before the COVID-19 crisis, we were already acutely aware of the need for a broader conversation around data privacy: look no further than the Snowden revelations, Cambridge Analytica, the New York Times Privacy Project, the General Data Protection Regulation (GDPR) in Europe, and the California Consumer Privacy Act (CCPA). In the age of COVID-19, these issues are far more acute. We also know that governments and businesses exploit crises to consolidate and rearrange power, claiming that citizens need to give up privacy for the sake of security. But is this tradeoff a false dichotomy? And what type of tools are being developed to help us through this crisis? In this episode, Katharine Jarmul, Head of Product at Cape Privacy, a company building systems to leverage secure, privacy-preserving machine learning and collaborative data science, will discuss all this and more, in conversation with Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp.Links from the showFROM THE INTERVIEWKatharine on TwitterKatharine on LinkedInContact Tracing in the Real World (By Ross Anderson)The Price of the Coronavirus Pandemic (By Nick Paumgarten)Do We Need to Give Up Privacy to Fight the Coronavirus? (By Julia Angwin)Introducing the Principles of Equitable Disaster Response (By Greg Bloom)Cybersecurity During COVID-19 ( By Bruce Schneier)

How can we better promote AI literacy? What skills are commonly lacking in organizations? Are new people entering the field considering bias in AI? In this episode of IBM thinkLeaders podcast, we are joined by guest Hugo Bowne-Anderson (Data Scientist at DataCamp) to discuss how we can better promote AI and digital skills. We talk to Hugo about the skills that people often overlook, how to build out successful teams, and how new people interested in AI can overcome hesitations. Connect with us @IBMthinkLeaders (#thinkLeaders) & our guest at: @hugobowne @DataCamp

This week Hugo speaks with Dr. Brandeis Marshall, about people of color and under-represented groups in data science. They’ll talk about the biggest barriers to entry for people of color, initiatives that currently exist and what we as a community can do to be as diverse and inclusive as possible.Brandeis is an Associate Professor of Computer Science at Spelman College. Her interdisciplinary research lies in the areas of information retrieval, data science, and social media. Other research includes the BlackTwitter Project, which blends data analytics, social impact and race as a lens to understanding cultural sentiments. Brandeis is involved in a number of projects, workshops, and organizations that support data literacy and understanding, share best data practices and broaden participation in data science.LINKS FROM THE SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWBrandeis on TwitterThe BlackTwitter ProjectThe Impact of Live Tweeting on Social Movements (By Brandeis Marshall, Takeria Blunt, Tayloir Thompson)EvergreenLP: Using a social network as a learning platform (By Brandeis Marshall, Jaye Nias, Tayloir Thompson, Takeria Blunt)Journal of Computing Sciences in Colleges (By Brandeis Marshall)DSX (Data Science eXtension Faculty development and undergraduate instruction in data science) African American Women Computer Science PhDs500 Women ScientistsBlack in AIWomen in Machine LearningFROM THE SEGMENTSWhat Data Scientists Really Do (with Hugo Bowne-Anderson & Emily Robinson ~21:30 & ~41:40)What Data Scientists Really Do, According to 35 Data Scientists (Harvard Business Review article by Hugo Bowne-Anderson)What Data Scientists Really Do, According to 50 Data Scientists (Slides from a talk by Hugo Bowne-Anderson)Original music and sounds by The Sticks.

For this episode, I speak with Hugo Bowne-Anderson; a data scientist at DataCamp (an educational platform for learning to code) and host of the DataFramed podcast. The idea for asking Hugo to appear on this episode, was to chat about learning a programming language. Because for some traders, having the ability to write code can have great advantages—such as having the ability to collect stats on market behavior, perform research in a robust data-driven way, visualize large amounts of data, backtest and analyse trading ideas, implement algorithmic strategies, etc. Plus more professional trading firms and finance related positions now require applicants to have some programming skills. And the same goes for many industries, which should be no surprise, considering a recent IBM study revealed that ‘90% of the world’s data has been created in the last two years alone.’ Hugo and I discuss when someone should consider learning to code, determining what’s relevant, the time it takes to become fluent in a programming language, working with new datasets, what to be wary of when using predictive models. And for fun, I ask Hugo (as a data scientist) how he’d go about creating a basic strategy…

We are super pumped to be launching a weekly data science podcast called DataFramed, in which Hugo Bowne-Anderson, a data scientist and educator at DataCamp, speaks with industry experts about what data science is, what it’s capable of, what it looks like in practice and the direction it is heading over the next decade and into the future. Check out this snippet for a sneak preview!