Today, my podcast's YouTube hit 100,000 subscribers. I'm way more excited, however, about this chart: We 10X'ed our watch time from ~100 hours per day to >1000 hours per day in 3 months. Here's how:
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Filtering by Tag: machine learning
In Case You Missed It in September 2025
The guests we had on my show in September were *extra* extraordinary. ICYMI, today's episode highlights the best parts of my convos with them:
1. Aurélien Géron, the bestselling ML author of all time, on why AGI may resist human control.
2. Shirish Gupta and Ishan Shah on how to make an A.I. hardware purchase that will still be relevant five years from now.
3. Renowned University of Oxford economics professor Carl Benedikt Frey on whether A.I. could allow for a billion-dollar company with a single employee.
4. David Loker on how CodeRabbit makes A.I. code-reviews more secure than with humans alone.
5. Graph guru Amy Hodler on the future of graph networks, including causal and multimodal functionality.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in August 2025
ICYMI, today's episode provides the top moments from conversations I had with my podcast guests in August. Interestingly, in August all of these episodes took off on YouTube in an unprecedented way:
Julien Launay, co-founder and CEO of the wildly successful Adaptive ML, explains how LLMs are trained including the pre-training and the increasingly critical post-training phases.
Michelle Yi provides her brilliant take on shocking misalignment research from Anthropic, which showed that A.I. agents will frequently resort to blackmailing humans when its goals are threatened.
Kirill Eremenko, CEO of the learning platform SuperDataScience, describes what it takes to become an A.I. Engineer.
And Akshay Agrawal who explains how his rapidly-adopted marimo notebook can be converted into a full-blown, click-and-point web app in *seconds*.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Hopes and Fears of AGI, with All-Time Bestselling ML Author Aurélien Géron
Aurélien Géron is the best-selling author of ML books of all time. Today's episode is his first interview in a *decade*... and it's a stunner! He reveals his next book as well as his deep thoughts on AGI.
Aurélien:
Is the author of O'Reilly's "Hands-On Machine Learning" series of books. The fourth edition in the series will be on bookshelves in the coming months.
Was Co-Founder and CTO of Wifirst, an exceptionally successful French tech company.
Was previously a product manager at Google.
Holds an MEng in Computer Science from AgroParisTech.
The start of today's episode (on Aurélien's books) may appeal primarily to hands-on practitioners like data scientists and software developers. The bulk of the episode, however, will appeal to anyone looking to understand how Artificial General Intelligence (AGI) will overhaul work, life and society for humans.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in July 2025
Wow, what a month... ICYMI, here are the top moments from the conversations I had with my podcast guests in July:
AI-lab director Lilith Bat-Leah on why data-centric machine learning research (DMLR) is the future of AI.
Prolific author and speaker Sinan Ozdemir (last week in San Francisco, I witnessed him deliver the *best* talk I've ever seen) on whether we can trust LLM benchmarks.
Bloomberg's Dr. Sebastian Gehrmann on why generic LLMs fail in regulated industries like finance and healthcare.
AI entrepreneur Dr. Zohar Bronfman on how AI can predict what you'll do next... before you're even consciously aware of your decision.
Microsoft AI researcher Dr. Robert Osazuwa Ness with step-by-step guidance on how to build causal AI models using PyTorch.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Future of Python Notebooks is Here, with Marimo’s Dr. Akshay Agrawal
I love Jupyter Notebooks... but they have a lot of painful "features". Today's guest Akshay Agrawal has built marimo, which resolves these issues and adds in lots of clever new innovations.
More on Akshay:
Co-founder and CEO of marimo.
Carried out a PhD in electrical engineering at Stanford University.
Previously held software engineering roles at Google and Netflix.
Today's episode will appeal most to hands-on practitioners. In it, we cover:
Why 96% of Jupyter notebooks fail to reproduce their original results.
How reactive notebooks can transform a simple slider adjustment into automatic recalculation across your entire analysis — like Excel for data science.
How you can now intuitively select data points with your mouse in a scatter plot and instantly get them back as a Python dataframe for analysis.
How one marimo notebook can simultaneously be an executable script, an importable Python module, and even a fully functional click-and-point UI!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in June 2025
We had exceptional guests on my podcast in June. In today's "In Case You Missed It" episode, hear the best parts of all my June convos. Here's a quick summary to tantalize you:
Strategy consultant Diane Hare with five tricks for gaining buy-in on A.I. transformation in your organization.
Renowned data-career educator Avery Smith on the two portfolio projects every aspiring data analyst should build.
SuperDataScience founder Kirill Eremenko on what you're missing if you're struggling to land an A.I. job.
San Fran-based venture capitalist Shaun Johnson on the traits that make a great A.I. startup founder.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Automating Legal Work with Data-Centric ML (feat. Lilith Bat-Leah)
Today, exceptional communicator Lilith Bat-Leah explains why "Data-Centric ML Research" trumps our typical focus on model capability, with examples from her extensive Legal A.I. background.
Lilith:
Has over a decade of experience specializing in the application of ML to legal tech.
Is Senior Director of A.I. Labs at Epiq, a leading LegalTech firm that has over 6000 employees.
Has published work on evaluation methods for the use of ML in legal discovery as well as on Data-centric ML Research (DMLR).
Is co-chair of the DMLR working group MLCommons and has organized DMLR workshops at [ICML] Int'l Conference on Machine Learning and ICLR, two of the most important A.I. conferences.
Holds a degree from Northwestern University, in which she focused on statistics.
Today’s episode will appeal primarily to hands-on practitioners like data scientists, AI/ML engineers and software developers.
In today’s episode, Lilith details:
How A.I. is revolutionizing the legal industry by automating up to 80% of traditional discovery processes.
Why 'elusion' is a critical metric that only exists in LegalTech — and what it reveals about machine learning evaluation.
The surprising reason why we should stop obsessing over model improvements and focus on something that takes up 80% data scientists’ time instead.
How she grew from being a temp receptionist to an A.I. lab director by falling in love with statistics.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in March 2025
We had absolutely killer guests and killer conversations on my podcast in March. This isn't bluster; I learned a ton from Andriy, Richmond, Natalie and Varun... Today's episode features all the best highlights!
The specific conversation highlights included in today's episode are:
The mega-bestselling author of "The 100-Page Machine Learning Book" (and now "The 100-Page Language Models Book"!) Dr. Andriy Burkov on the missing piece of AGI: Why LLMs can't plan or self-reflect.
Relatedly, the fascinating and exceptionally well-spoken Natalie Monbiot contrasted artificial intelligence with the human variety, detailing what makes us unique.
The charismatic software engineer Richmond Alake (of MongoDB) explained his "A.I. Stack" concept and how you can leverage it to build better A.I. applications.
Former Google Gemini engineer Varun Godbole provides a helpful overview of guide to neural network design, the (freely available!) "Deep Learning Tuning Playbook".
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI Should Make Humans Wiser (But It Isn’t), with Varun Godbole
Today's trippy, brain-stimulating episode features Varun Godbole, a former Google Gemini LLM researcher who’s turned his attention to the future implications of the crazy-fast-moving exponential moment we're in.
Varun:
Spent the past decade doing Deep Learning research at Google, across pure and applied research projects.
For example, he was co-first author of a Nature paper where a neural network beat expert radiologists at detecting tumors.
Also co-authored the Deep Learning Tuning Playbook (that has nearly 30,000 stars on GitHub!) and, more recently, the LLM Prompt Tuning Playbook.
He's worked on engineering LLMs so that they generate code and most recently spent a few years as a core member of the Gemini team at Google.
Holds a degree in Computer Science as well as in Electrical and Electronic Engineering from The University of Western Australia.
Varun mostly keeps today’s episode high-level so it should appeal to anyone who, like me, is trying to wrap their head around how vastly different society could be in a few years or decades as a result of abundant intelligence.
In today’s episode, Varun details:
How human relationship therapy has helped him master A.I. prompt engineering.
Why focusing on A.I. agents so much today might be the wrong approach — and what we should focus on instead.
How the commoditization of knowledge could make wisdom the key differentiator in tomorrow's economy.
Why the future may belong to "full-stack employees" rather than traditional specialized roles.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in February 2025
February was another insane month on my podcast. In addition to having stunning smiles, all four guests I hosted are fascinating, highly knowledgeable experts. Today's episode features highlights of my convos with them.
The specific conversation highlights included in today's episode are:
Professional-athlete-turned-data-engineer Colleen Fotsch on how DBT simplifies data modeling and documentation.
Engineer-turned-entrepreneur Vaibhav Gupta on the new programming language, BAML, he created for AI applications. He details how BAML will save you time and a considerable amount of money when calling LLM APIs.
Professor Frank Hutter on how TabPFN, the first deep learning approach to become the state of the art for modeling tabular data (i.e., the structured rows and columns of data that, until now, deep learning was feeble at modeling).
The ebullient Cal Al-Dhubaib on the keys to scaling (and selling!) a thriving data science consultancy.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
LLMs and Agents Are Overhyped, with Dr. Andriy Burkov
Andriy Burkov's ML books are mega-bestsellers and his newsletter has a wild 900,000 subscribers. He seldom does interviews so don't miss today's episode, in which he takes compelling, contrarian views on LLMs and agents.
More on Dr. Burkov:
His indispensable "100-Page Machine Learning Book" seems to be on *every* data scientist / ML engineer's bookshelf.
He also wrote "ML Engineering" and his latest book, "The 100-Page Language Model Book", was released this year to rave reviews.
His "Artificial Intelligence" newsletter is subscribed to by 900,000 people on LinkedIn.
He's the Machine Learning Lead at TalentNeuron, a global labor-market analytics provider.
He runs his own book-publishing company, True Positive Inc.
Previously held data science / ML roles at Gartner, Fujitsu and more.
Holds a PhD in Computer Science (A.I.) from Université Laval in Quebec, where his doctoral dissertation focused on multi-agent decision-making — 15 years ago!
Despite Dr. Burkov being such a technical individual, most of today’s episode should appeal to anyone interested in A.I. (although some parts here and there will be particularly appealing to hands-on machine-learning practitioners).
In today’s episode, Andriy details:
Why he believes AI agents are destined to fail.
How he managed to create a chatbot that never hallucinates — by deliberately avoiding LLMs.
Why he thinks DeepSeek AI crushed Bay Area A.I. leaders like OpenAI and Anthropic.
What makes human intelligence unique from all other animals and why A.I. researchers need to crack this in order to attain human-level intelligence in machines.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How to Grow (and Sell) a Data Science Consultancy, with Cal Al-Dhubaib
Today, my ebullient long-time friend Cal Al-Dhubaib makes his debut on my podcast to spill the beans on how you can launch your own thriving (data science / A.I. / ML) consultancy and, eventually, sell it 💰
Cal:
Is Head of AI & Data Science at Further, a data and A.I. company based in Atlanta that has hundreds of employees.
Previously, he was founder and CEO of Pandata, an Ohio-based A.I. and machine learning consultancy that he grew for over eight years until it was acquired by Further a year ago.
Delivers terrific talks — don’t miss him if you have the chance!
Holds a degree in data science from Case Western Reserve University in Cleveland.
Today’s episode should appeal to any listener, particularly anyone that would like to drive revenue and profitability from data science or AI projects.
In it, Cal covers:
Why his first startup was unsuccessful, but how the experience allowed him to discover an untapped market and build Pandata, a thriving data science consultancy.
His unconventional strategy of requiring clients to make a sizable commitment up front that initially scared away clients but ultimately attracted the best ones.
The way core values inspired by his "tin can to Mars" thought experiment shaped his hiring and company culture.
How making data science "boring", helping his clients trust AI systems and delivering a clear return on investment became his formula for success.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
BAML: The Programming Language for AI, with Vaibhav Gupta
Today's guest, Vaibhav Gupta, has developed BAML, the programming language for AI. If you are calling LLMs, you've gotta check BAML out for instant accuracy improvements and big (20-30%) cost savings.
More on charming and terrifically brilliant Vaibhav:
Founder & CEO of Boundary (YC W23), a Y Combinator-backed startup that has developed a new programming language (BAML) that makes working with LLMs easier and more efficient for developers.
Across his decade of experience as a software engineer, he built predictive pipelines and real-time computer vision solutions at Google, Microsoft and the renowned hedge fund The D. E. Shaw Group.
Holds a degree in Computer Science and Electrical Engineering from The University of Texas at Austin.
This is a relatively technical episode. The majority of it will appeal to folks who interact with LLMs or other model APIs hands-on with code.
In today’s information-dense episode, Vaibhav details:
How his company pivoted 13 times before settling upon developing a programming language for A.I.
Why creating a programming language was "really dumb" but why it’s turning out to be brilliant, including by BAML already saving companies 20-30% on their AI costs.
Fascinating parallels between today's A.I. tools and the early days of web development.
His unconventional hiring process (I’ve never heard of anything remotely close to it) and the psychology behind why it works.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in December 2024
Today's "In Case You Missed it Episode"... is one not to miss! Several of the most fascinating conversations I've ever had on the SuperDataScience Podcast I host happened in December.
The specific conversation highlights included in today's episode are:
1. The legendary Dr. Andrew Ng on why LLM cost doesn't matter for your A.I. proof of concept.
2. Building directly on Andrew's segment, CTO (and my fellow Nebula.io co-founder) Ed Donner on how to choose the right LLM for a given application.
3. Extremely intelligent and clear-spoken Dr. Eiman Ebrahimi (CEO of Protopia AI) on the future of autonomous systems and data security in our Agentic A.I. future.
4. From our 2024 recap episode, Sadie St. Lawrence's three biggest A.I. "wow" moments of the year... as well as the biggest flop of the year. (One company was behind both!)
5. Harvard/MIT humanist chaplain Greg Epstein (and bestselling author on tech in society) on the ethics of accelerating A.I. advancements. Should we, for example, consider slowing A.I. progress down?
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
2025 AI and Data Science Predictions, with Sadie St. Lawrence
Happy New Year! To prepare you for 2025, today's guest is the clairvoyant Sadie St. Lawrence, who predicts what the biggest A.I. trends will be in the year ahead. We also pick the A.I. winners and losers of 2024.
In a bit more detail, in today’s episode (which will appeal to technical and non-technical listeners alike):
• We cover how Sadie’s predictions for 2024 (which she made a year ago on this show) panned out.
• We award our “wow moment” of 2024, our comeback of the year, our disappointment of the year and our overall winner of 2024.
• And then, of course, we speculate on the five biggest trends to prepare for in 2025.
As with our 2022, 2023 and 2024 predictions episode, our special guest again this year is Sadie St. Lawrence, who is:
• A data science and machine learning instructor whose content has been enjoyed by over 600,000 students.
• The Founder and CEO of the Human Machine Collaboration Institute as well as being founder and chair of Women In Data™️, a community of over 60,000 women across 55 countries.
• Serves on multiple start-up boards.
• Hosts the Data Bytes podcast.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in November 2024
We had a ton of laughs and I had some seriously mind-expanding moments thanks to my guests on the SuperDataScience Podcast last month. ICYMI, today's episode highlights the most riveting moments from November.
The specific conversation highlights included in today's episode are:
Deepali Vyas, Global Head of Data and A.I. at executive-search giant Korn Ferry, on how A.I. is transforming recruitment and how job-seekers can stay ahead of the curve.
Jess Ramos, data analyst and leading content creator on data careers, on where to start if you yourself are seeking a career in data.
Bryan McCann, co-founder and CTO of the rapidly-scaling A.I. platform You.com, on why machines will make much better scientists than humans... and how they will surpass human scientists surprisingly soon.
Martin Goodson, CEO of the prestigious British A.I. firm Evolution AI, on how the public figures who are speaking most loudly about A.I. are probably not the people we should be listening to.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI Systems as Productivity Engines, with You.com’s Bryan McCann
Today, wildly intelligent Bryan McCann describes the Agentic A.I. behind his skyrocketing startup You.com and how it will lead to scientific discoveries human scientists couldn't dream of making. Don't miss this episode!
Bryan:
• Co-Founder and CTO of You.com, a prominent Bay Area A.I. startup that has raised $99m in venture capital (including a $50m Series B in September that valued the firm at nearly a billion dollars).
• Was previously Lead Research Scientist at Salesforce and an assistant on courses at Stanford such as Andrew Ng’s wildly popular machine learning course.
• Holds a Master’s in Computer Science, a Bachelor’s in Computer Science and a Bachelor’s in Philosophy, all from Stanford University.
Today’s episode should be fascinating to anyone interested in AI. In it, extremely well-spoken Bryan details:
• The philosophical underpinnings of the breakthroughs that led to the leading A.I. models we have today as well as the ones that will emerge in the coming years.
• How a coding mistake he made serendipitously revealed fundamental insights about meaning and language model alignment.
• Why he believes humanity is entering an existential crisis due to A.I., but nevertheless remains optimistic about the future.
• The fascinating connection between language models and biological proteins.
• Why A.I. systems might soon be able to make scientific discoveries humans could never dream of making.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The 10 Reasons AI Projects Fail, with Dr. Martin Goodson
Most A.I. projects fail. In today's episode, the brilliant (and hilarious) Dr. Martin Goodson details the top 10 reasons why A.I. projects fail and how to avoid these common pitfalls.
Martin:
• Is CEO and Chief Scientist at Evolution AI, a firm that uses generative A.I. to extract information from millions of documents a day for their clients.
• Is Founder and Organizer of the London ML Meetup, which (with >15,000 members) is the largest community of AI/ML experts in Europe.
• Previously led data science at startups that apply ML to billions of data points daily.
• Was a statistical geneticist at the University of Oxford (where we shared a small office together)!
Today’s episode will be of interest to anyone even vaguely interested in data science, ML or AI. In today’s episode, Martin details:
• The 10 reasons why data science projects fail and how to avoid these common pitfalls.
• His insights on building A.I. startups that serve large enterprises.
• The importance of open-source A.I. development.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
PyTorch Lightning, Lit-Serve and Lightning Studios, with Dr. Luca Antiga
Lightning AI makes tons of tools that speed A.I. model dev and deployment, including the wildly popular open-source library PyTorch Lightning. Today, hear from hands-on CTO Dr. Luca Antiga how all the magic happens ⚡️
More on Luca:
CTO of Lightning AI, which (as one of world’s hottest startups developing A.I. tools) have raised over $80m in venture capital.
Is also CTO of OROBIX, an A.I. services company that Luca co-founded 15 years ago.
Holds a PhD in biomedical engineering from Politecnico di Milano… and did his postdoc at the Robarts Research Institute in London, Ontario (coincidentally around the same time I was doing brain-imaging research there).
Today’s episode will probably appeal most to hands-on practitioners like data scientists, software developers and ML engineers, but any tech-savvy professional could find it valuable.
In today’s episode, Luca details:
How Lightning AI's suite of tools (in addition to PyTorch Lightning, this includes Lightning Studios, LitServe and the Thunder Compiler) is making A.I. development faster and easier.
The rise of small language models and their potential to rival LLMs.
His journey from biomedical imaging to deep learning pioneer.
How software developer’s work will be transformed by A.I. in the coming years.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.