Python Polars: The Definitive Guide, with Jeroen Janssens and Thijs Nieuwdorp

Today's episode on Polars is in equal parts hilarious and informative with Jeroen and Thijs, who co-authored the brand-new O'Reilly book "Python Polars: The Definitive Guide". Enjoy this one!

GUESTS

More on Dr. Jeroen Janssens:

• Senior Developer Relations Engineer at Posit PBC (iconic creators of RStudio and much more).

• Previously, was Senior Machine Learning Engineer at Xomnia.

• Wrote the invaluable O’Reilly book "Data Science at the Command Line".

• Holds a PhD in machine learning from Tilburg University.

...and on Thijs Nieuwdorp:

• Lead Data Scientist at Xomnia, the largest Dutch data and A.I. consulting company.

• Holds a degree in A.I. from Radboud University.

TARGET AUDIENCE

Today’s episode will be particularly appealing to hands-on data science, machine learning and A.I. practitioners but Jeroen and Thijs are tremendous storytellers and frankly very funny so this episode can probably be enjoyed by anyone interested in data and A.I.

TOPICS

In today’s episode, Jeroen and Thijs detail:

• Why pandas users are rapidly switching to Polars for dataframe operations in Python.

• The inside story of how O'Reilly rejected four book proposals on Polars before accepting the fifth.

• The moment when an innocuous GitHub pull request forced a complete rewrite of an entire book chapter.

• A previously secret collaboration with NVIDIA and Dell that revealed remarkable GPU acceleration benchmarks by Polars.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Calling Clinicians: Help Us Build the Future of AI Therapy

I recently began supervising a PhD student in the Auckland robotic-engineering department and we are looking to partner with psychotherapists to develop a companion robot. Do you know anyone relevant/interested?

(I promise that our eventual robotic solution will not be a two-headed monstrosity featuring my face on a kiwi bird's body... but maybe it helped get your attention 😂)

Through several years of upcoming R&D at The University of Auckland (I will mostly be supervising remotely from New York!), our project aims to develop a therapeutic A.I. model (e.g., a multi-modal Large Language Model) to power the conversational, perceptual and (potentially) real-time video-generation capabilities of a companion robot that gives its user (which could be in a clinical or at-home setting) personalized therapy and support when a human therapist is unavailable.

A particularly prominent challenge for us in developing and testing this LLM (and, eventually, robotic embodiment) is access to data from real therapeutic conversations, although there are other immediate and long-term R&D challenges that we would love practicing therapists to help us with as well.

This is an exciting, impactful project that could markedly improve millions of lives around the world in the coming decades. I applaud PhD candidate Maryam Khakpour for tackling it head on! If you're a clinician who's keen to be involved with the A.I. revolution, now's your chance :)

Blackwell GPUs Are Now Available at Your Desk, with Sama Bali and Logan Lawler

Today's charming and complementary guests — Sama Bali from NVIDIA and Logan Lawler from Dell — make for an extra fun episode on the powerful new Blackwell GPUs... now available at your desk!

More on Sama:

• A.I. Solutions leader at NVIDIA that specializes in bringing A.I. products to market.

• Prior to NVIDIA, held a Machine Learning Solutions role at Amazon Web Services (AWS).

• Focused on educating data scientists and developers on A.I. innovations and implementing them effectively in enterprises.

• Holds a Masters in Engineering Management from San José State University.

More on Logan:

• Leads Dell Pro Max A.I. Solutions (if you haven’t heard of Pro Max before, we’ll cover that in this episode!)

• Over his sixteen-year tenure at Dell Technologies, has held positions across merchandising, services, marketing and e-commerce.

• Holds an MBA in management from Texas State University.

Today’s episode will be particularly appealing to hands-on data science, machine learning and A.I. practitioners but it isn’t especially technical and so can be enjoyed by anyone!

In today’s episode, Sama and Logan detail:

• Why data scientists are camping out at 6AM to attend NVIDIA's GTC conference.

• The killer specs of NVIDIA’s next-generation Blackwell GPUs.

• How Dell and Nvidia have joined forces to bring server-level AI power right to your desktop.

• How microservices are revolutionizing A.I. development and deployment.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Beyond GPUs: The Power of Custom AI Accelerators, with Emily Webber

The mind-blowing A.I. capabilities of recent years are made possible by vast quantities of specialized A.I.-accelerator chips. Today, AWS's (brilliant, amusing and Zen!) Emily Webber explains how these chips work.

Emily:

• Is a Principal Solutions Architect in the elite Annapurna Labs ML service team that is part of Amazon Web Services (AWS).

• Works directly on the Trainium and Inferentia hardware accelerators (for, respectively, training and making inferences with A.I. models).

• Also works on the NKI (Neuron Kernel Interface) that acts as a bare-metal language and compiler for programming AWS instances that use Trainium and Inferentia chips.

• Wrote a book on pretraining foundation models.

• Spent six years developing distributed systems for customers on Amazon’s cloud-based ML platform SageMaker.

• Leads the Neuron Data Science community and leads the technical aspects for the “Build On Trainium” program — a $110m credit-investment program for academic researchers.

Today’s episode is on the technical side and will appeal to anyone who’s keen to understand the relationship between today’s gigantic A.I. models and the hardware they run on.

In today’s episode, Emily details:

• The little-known story of how Annapurna Labs revolutionized cloud computing.

• What it takes to design hardware that can efficiently train and deploy models with billions of parameters.

• How Tranium2 became the most powerful A.I. chip on AWS.

• Why AWS is investing $110 million worth of compute credits in academic AI research.

• How meditation and Buddhist practice can enhance your focus and problem-solving abilities in tech.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Serverless, Parallel, and AI-Assisted: The Future of Data Science is Here, with Zerve’s Dr. Greg Michaelson

What are "code nodes" and "RAG DAGs"? Listen to today's episode with the highly technical (but also highly hilarious) Dr. Greg Michaelson to get a glimpse into the future of data science and A.I. model development.

Greg:

• Is a Co-Founder of Zerve AI, a super-cool platform for developing and delivering A.I. products that launched to the public on this very podcast a little over a year ago.

• Previously spent 7 years as DataRobot’s Chief Customer Officer and 4 years as Senior Director of Analytics & Research for Travelers.

• Was a baptist pastor while he obtained his PhD in Applied Statistics!

Today’s episode is on the technical side and so will appeal most to hands-on practitioners like data scientists, AI/ML engineers and software developers… but Greg is such an engaging communicator that anyone interested in how the practice of data science is rapidly being revolutionized may enjoy today’s episode.

In it, Greg details:

• How Zerve's collaborative, graph-based coding environment has matured over the past year, including their revolutionary 'Fleet' feature (in beta) that allows massive parallelization of code execution without additional cost.

• How AI assistants are changing the coding experience by helping build, edit, and connect your data science projects.

• Why the rise of LLMs might spell trouble for many SaaS businesses as building in-house solutions becomes increasingly viable.

• The innovative ways companies are using retrieval-augmented generation (RAG) to create more powerful A.I. applications.

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:

1. 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.

2. Relatedly, the fascinating and exceptionally well-spoken Natalie Monbiot contrasted artificial intelligence with the human variety, detailing what makes us unique.

3. 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.

4. 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.

The Neural Processing Units Bringing AI to PCs, with Shirish Gupta

In many situations, it's impractical (or even impossible!) to have A.I. executed in the cloud. In today's episode, Shirish Gupta details when to run A.I. locally and how Neural Processing Units (NPUs) make it practical.

Today's episode is about efficiently designing and deploying AI applications that run on the edge. Our guide on that journey is SuperDataScience Podcast fan, Shirish! Here's more on him:

• Has spent more than two decades working for the global technology juggernaut, Dell Technologies, in their Austin, Texas headquarters.

• Has held senior systems engineering, quality engineering and field engineering roles.

• For the past three years, has been Director of AI Product Management for Dell’s PC Group.

• Holds a Master’s in Mechanical Engineering from the University of Maryland.

Today’s episode should appeal to anyone who is involved with or interested in real-world A.I. applications.

In this episode, Shirish details:

• What Neural Processing Units (NPUs) are and why they're transforming A.I. on edge devices.

• Four clear, compelling reasons to consider moving AI workloads from the cloud to your local device.

• The "A.I. PC" revolution that's bringing A.I. acceleration to everyday laptops and workstations.

• What kinds of Large Language Models are best-suited to local inference on AI PCs.

• How Dell's Pro A.I. Studio toolkit will drastically reduce enterprise A.I. deployment time.

• Plenty of real-life A.I. PC examples, including how a healthcare provider achieved physician-level accuracy with a custom vision model.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

How Semiconductors Are Made (And Fuel the AI Boom), with Kai Beckmann

Today's episode is an important one on the hardware that underlies all computing and is fueling the A.I. boom. It’s hard to imagine a better guest than Kai Beckmann for this essential topic.

Kai:

• Is Member of the Executive Board of Merck KGaA, Darmstadt, Germany (a 350-year-old firm that’s the world's oldest chemical and pharmaceutical company and that has more than 62,000 employees across 60 countries).

• Having worked at the gigantic firm for over 35 years, he’s been CEO of their Electronics business for the past eight years.

• Under his leadership, Merck KGaA develops cutting-edge, materials-based solutions and equipment for leading chip companies — 99% of electronic devices contain one of their products 🤯

• A leading speaker within the semiconductor industry, he’s an expert in material-based semiconductor solutions, A.I., digitalization, and change management.

Today’s episode will be of interest to anyone looking to understand the hardware that all of computing and data science depend on. In it, Kai details:

• How materials from one company are found in virtually every electronic device on the planet.

• How A.I. is being used to develop materials that power... more A.I.

• His vinyl-record analogy for understanding computer-chip manufacturing.

• The impact that scaled-up, stable quantum computing will have on society.

• How a neuromorphic chip might someday run on the power of a low-wattage light bulb while matching human brain capabilities.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Become Your Best Self Through AI Augmentation — feat. Natalie Monbiot

The deep-thinking and highly articulate Natalie Monbiot returns to my podcast today for a can't-miss episode (one of my favorite convos ever) on how A.I. will overhaul our lives, our work, our society in the coming years.

More on Natalie:

• Through her consultancy, Virtual Human Economy, she advises on virtual humans and A.I. clones, including to startups like Wizly and investment firms like Blue Tulip Ventures.

• Was previously Head of Strategy at Hour One, a leading virtual-human video-generation startup.

• Regularly speaks at the world's largest conferences, including Web Summit and SXSW.

• Holds a Master's in Modern Languages and Literature from the University of Oxford.

Today’s fascinating episode will be of great interest to all listeners. In it, Natalie details:

• How A.I. is making us dumber — and what we can do about it.

• Why the "virtual human economy" could be the next evolution of human civilization.

• The two states of being humans are seeking (and how A.I. could help us achieve them).

• Why focusing on merely 10x’ing our capabilities misses the much bigger opportunity of A.I.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

NoSQL Is Ideal for AI Applications, with MongoDB’s Richmond Alake

In today's episode (#871), I'm joined by the gifted writer, speaker and ML developer Richmond Alake, who details what NoSQL databases are and why they're ideally suited for A.I. applications.

Richmond:

• Is Staff Developer Advocate for AI and Machine Learning at MongoDB, a huge publicly-listed database company with over 5000 employees and over a billion dollars in annual revenue.

• With Andrew Ng, he co-developed the DeepLearning.AI course “Prompt Compression and Query Optimization” that has been undertaken by over 13,000 people since its release last year.

• Has delivered his courses on Coursera, DataCamp, and O'Reilly.

• Authored 200+ technical articles with over a million total views, including as a writer for NVIDIA.

• Previously held roles as an ML Architect, Computer Vision Engineer and Web Developer at a range of London-based companies.

• Holds a Master’s in computer vision, machine learning and robotics from The University of Surrey in the UK.

Today's episode (filmed in-person at MongoDB's London HQ!) will appeal most to hands-on practitioners like data scientists, ML engineers and software developers, but Richmond does a stellar job of introducing technical concepts so any interested listener should enjoy the episode.

In today’s episode, Richmond details:

• How NoSQL databases like MongoDB differ from relational, SQL-style databases.

• Why NoSQL databases like MongoDB are particularly well-suited for developing modern A.I. applications, including Agentic A.I. applications.

• How Mongo incorporates a native vector database, making it particularly well-suited to RAG (retrieval-augmented generation).

• Why 2025 marks the beginning of the "multi-era" that will transform how we build A.I. systems.

• His powerful framework for building winning A.I. strategies in today's hyper-competitive landscape.

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:

1. Professional-athlete-turned-data-engineer Colleen Fotsch on how DBT simplifies data modeling and documentation.

2. 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.

3. 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).

4. 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.