Jon Krohn speaks with Shashank Kalanithi, the man who makes a sport out of YouTube and data analytics out of sports. Listen in as he talks about how he got started producing YouTube videos on data science, the essential differences between data science roles, and how data could shape the future of the sports industry.

This episode is brought to you by Datalore (https://datalore.online/SDS), the collaborative data science platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What motivated Shashank to start his YouTube channel [04:31]
• The must-have technical skills for every data scientist [16:59]
• The soft skills needed for data science [20:52]
• The differences between data analyst, data scientist and data engineer [24:26]
• How data are currently being applied in the sports industry [38:38]
• The “needs” divide between digital native and traditional companies [45:34]

Additional materials: www.superdatascience.com/623

The Super Data Science Podcast: Machine Learning | AI | Success

623: Data Analyst, Data Scientist, and Data Engineer Career Paths

AI visionary and CEO of SingularityNET Dr. Ben Goertzel provides a deep dive into the possible realization of Artificial General Intelligence (AGI) within 3-7 years. Explore the intriguing connections between self-awareness, consciousness, and the future of Artificial Super Intelligence (ASI) and discover the transformative societal changes that could arise.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au), the AWS Insiders Podcast (https://pod.link/1608453414), and by Modelbit (https://modelbit.com), for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Decentralized and benevolent AGI [03:13]
• The SingularityNET ecosystem [13:10]
• Dr. Goertzel's vision for realizing AGI - combining DL with neuro-symbolic systems, genetic algorithms and knowledge graphs [25:50]
• How reaching AGI will trigger Artificial Super Intelligence [38:51]
• Dr. Goertzel's approach to AGI using OpenCog Hyperon [42:34]
• Why Dr. Goertzel believes AGI will be positive for humankind [53:07]
• How to ensure the AGI is benevolent [1:06:43]
• How AGI or ASI may act ethically [1:13:50]

Additional materials: www.superdatascience.com/697

697: The (Short) Path to Artificial General Intelligence, with Dr. Ben Goertzel

Daniel Rock is an economist and Assistant Professor at the Wharton School of the University of Pennsylvania. He specializes in studying the economic impacts of AI and other transformative technologies, focusing on productivity, labor markets, and the role of intangible assets. Daniel is also a co-founder of WorkHelix, a startup dedicated to helping organizations accelerate AI adoption by identifying high-value use cases and providing actionable roadmaps.
In this episode, Daniel shares his insights on the transformative power of generative AI, the challenges of adoption, and the opportunities for rethinking productivity and economic structures. He also delves into his research on the "Productivity J-Curve" and discusses how businesses can prepare for the rapidly evolving AI landscape.

Key Topics Discussed
- Breaking Work into Tasks: How AI impacts specific tasks within job roles rather than entire roles.
- The "Productivity J-Curve": Why transformative technologies like AI take time to show measurable productivity gains.
- General-Purpose Technologies: Lessons from historical technologies like electricity in understanding AI’s economic potential.
- Complementary Innovations: The critical role of training, workflows, and intangible assets in maximizing AI’s benefits.
- AI Adoption Challenges: Why large firms dominate AI adoption and the barriers smaller companies face.
- Automation vs. Augmentation: How organizations can balance automating repetitive tasks while enhancing worker productivity.
- WorkHelix: Daniel’s startup and its mission to accelerate AI adoption by analyzing task-level opportunities and tracking organizational impact.
- Career Advice for AI Practitioners: Staying on the steep end of the learning curve and leveraging AI tools to build adaptable careers.

Memorable Takeaways
- AI changes tasks, not entire jobs. It’s about where it changes what people do.
- The Productivity J-Curve shows that big technology investments take time to pay off.
- To get real returns from AI, complementary innovations aren’t optional—they’re necessary.
- Big firms succeed with AI because they’ve mastered the tech stack, from data science to cloud.
- Stay on the steep end of the learning curve—adaptability is the best skill.


Links and Resources
Research and Papers
- WorkHelix Research: https://www.workhelix.com/#research
- "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models" by Daniel Rock and co-authors: https://arxiv.org/pdf/2303.10130
- Generative AI at Work by Erik Brynjolfsson, Danielle Li & Lindsey R. Raymond: https://www.nber.org/papers/w31161
- "Artificial Intelligence and the Modern Productivity Paradox" by Erik Brynjolfsson, Chad Syverson, and Daniel Rock: https://www.nber.org/system/files/working_papers/w24001/w24001.pdf
- Paul David’s “The Dynamo and the Computer”: https://www.researchgate.net/publication/4724731_The_Dynamo_and_the_Computer_An_Historical_Perspective_On_the_Modern_Productivity_Paradox
- Daron Acemoglu’s Resarch:  https://www.nber.org/system/files/working_papers/w16082/w16082.pdf
- Pascual Restrepo’s Research: http://pascual.scripts.mit.edu/research/
Connect with Daniel Rock
- Linkedin https://www.linkedin.com/in/daniel-rock-61252485/
- Google Scholar: Daniel Rock’s Research: https://scholar.google.com/citations?user=2ftcO0wAAAAJ&hl=en
- WorkHelix : https://www.workhelix.com/
- Twitter: https://x.com/danielrock 
- BlueSky: https://bsky.app/profile/danielrock.bsky.social

This episode was sponsored by:  
Ai+ Training https://aiplus.training/ 
Home to hundreds of hours of on-demand, self-paced AI training, ODSC interviews, free webinars, and certifications in in-demand skills like LLMs and Agentic AI

And created in partnership with ODSC https://odsc.com/ 
The Leading AI Builders Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps

AI at Work: Breaking Down Tasks, Transforming Jobs, and Driving Innovation with Daniel Rock

In this episode of the ODSC AI Podcast, host Sheamus McGovern, founder of ODSC, sits down with Hugo Shi, Co-Founder and CTO of Saturn Cloud, a platform that gives data scientists and ML engineers the tools and flexibility they need to work the way they want. From his early days as a desk quant during the 2008 financial crisis to founding Saturn Cloud, Hugo brings a wealth of experience across finance, open source, and AI infrastructure.

This conversation dives deep into the realities of building AI infrastructure at scale, advocating for self-service tools for AI practitioners, managing cloud costs, and why flexibility—not control—is the foundation for productive data teams. It’s a must-listen for anyone working with machine learning infrastructure, whether you're a beginner navigating your first platform or a seasoned engineer scaling multi-cloud operations.

🌟 Key Topics Covered:
- Hugo’s career journey: From quant finance to co-founding Anaconda and then Saturn Cloud
- What working as a desk quant during the 2008 crisis taught him about speed, impatience, and iteration
- The pivotal role Anaconda played in democratizing Python data science
- Why Saturn Cloud was founded: common infra pain points across data teams
- How Saturn Cloud empowers teams through:
- - Interactive compute environments
- - Scheduled jobs
- - Long-running deployments
- The importance of flexibility vs. opinionated platforms 
- Why data scientists should not suffer in silence over infra pain
- Hidden cloud costs: compute, storage, and network—and how to manage them
- Differences between AI cloud providers (CoreWeave, Lambda Labs) and traditional hyperscalers (AWS, Azure, GCP)
- Scaling AI: lessons from working with massive clusters and thousands of parallel jobs
- Security best practices in ML platforms, including role-based access and cost attribution
- Why ML teams should collaborate across IT, product, and data engineering
- Hard-won lessons from real-world AI infrastructure scaling

💬 Memorable Outtakes:
On infrastructure friction and self-advocacy: “Data scientists, ML engineers, and AI engineers suffer in silence… They don’t perceive themselves as tech experts, so they think they have to accept infrastructure pain. They shouldn’t.”
On why Saturn Cloud avoids being too opinionated: “Notebooks are fine—but making them the only way to work? That’s a career trap. People should graduate to full IDEs and better practices.”
On scaling AI operations: “What can be done, will be done. If it’s possible, someone will try it. At scale, low-probability failures become inevitable.”

💻 References & Resources:
Hugo Shi:
- LinkedIn: https://www.linkedin.com/in/hugo-shi
- CTO & Co-Founder, Saturn Cloud: https://www.saturncloud.io/
Mentioned Companies and Tools:
- Saturn Cloud: https://www.saturncloud.io/
- Anaconda: https://www.anaconda.com/
- Phoenix Framework( For evaluation) https://github.com/phoenixframework/phoenix
- Prometheus (for resource monitoring): https://prometheus.io/
- CoreWeave: https://www.coreweave.com/
- Lambda Labs: https://lambdalabs.com/
- Neurelo (Neas): https://www.neurelo.com/
- RunPod: https://www.runpod.io/
- Cursor IDE: https://www.cursor.so/
- Streamlit: https://streamlit.io/
- Jupyter: https://jupyter.org/
- PyCharm: https://www.jetbrains.com/pycharm/

🤝 Sponsored by:
Agentic AI Summit 2025
Join the premier virtual event for AI builders from July 15–30. Gain hands-on skills in designing, deploying, and scaling autonomous AI agents.
🔥 Use code podcast for 10% off any ticket.
Register now: https://www.summit.ai/
ODSC West 2025 – The Leading AI Training Conference
Attend in San Francisco from October 28–30 for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
🔥 Use code podcast for 10% off any ticket.
Learn more: https://odsc.com/california

What No One Tells You About AI Infrastructure with Hugo Shi

DAX in my veins (Vocal mix)

In this episode of the ODSC Podcast, host Sheamus McGovern is joined by Veronika Durgin, Vice President of Data at Saks. Veronika brings over two decades of experience in data engineering, platform architecture, data modeling, and analytics. She joins us to expand on her ODSC presentation, The 10 Most Neglected Data Engineering Tasks, offering practical insights into often-overlooked practices that can make or break modern data systems. From defining what “done” really means to preparing for seasonality and building self-recovering pipelines, Veronika shares hard-won lessons, entertaining stories, and a deep well of practical advice.

💻 Key Topics Covered:
- Veronika’s unconventional career journey from biology to data leadership
- The “bridge strategy” in build vs. buy decisions and how it improves agility
- Why “definition of done” must include more than just working code
- The value of empathy: walking a mile in the business team’s shoes
- The dangers of ignoring business seasonality in data pipelines
- What makes a truly self-healing data pipeline
- Testing with production data vs. data in production
- Handling date/time data with precision and naming conventions
- The forgotten bucket of work: tech debt, improvements, and innovation
- The environmental impact of data systems and responsible engineering
- The rise of “vibe coding” and the challenges it poses for data engineering

💬 Memorable Outtakes:
- “If you don't know why you're working on this and who needs it, you shouldn't be working on it.” 
- “Everything we build today is tomorrow's legacy. Build things to be replaceable.”  
- “We automate, not because we’re lazy—but because we care about sleeping at night.” 

🖥 References & Resources:
- Veronika Durgin on LinkedIn: https://www.linkedin.com/in/vdurgin/
- Veronika Durgin on Medium: https://veronikadurgin.medium.com
- The Phoenix Project (pdf of book): https://shorturl.at/Ce5gA
- “Definition of Done”: https://medium.com/@durginv/definition-of-done-eea52c472cc3
- Tsedal Neeley Digital Mindset (book referenced): https://www.tsedal.com/book/the-digital-mindset/
- Stargate AI data center project (referenced during energy discussion): https://openai.com/index/announcing-the-stargate-project/

Sponsored by:
🔥 ODSC West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
Use the code podcast for 10% off any ticket.
Learn more: https://odsc.com/california

The Most Neglected Tasks in Data Engineering with Veronika Durgin

In this session of the Super Data Science Podcast, I chat with Ruben Kogel who has done some amazing data analytics in the startup space.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/1

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 001 : Self-serve Analytics, Data Science MBA and Managing a Team of Analysts with Ruben Kogel

In this episode of the SuperDataScience Podcast, I chat with the President and Editor at KDNuggets, Gregory Piatetsky-Shapiro. You will talk about the recent advancements in Data Science, know how Reinforcement Learning can greatly improve the AI capabilities and discuss the noticeable growing trend of automation in the field of data science.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/175

SDS 175: Insights from the Founder of KDnuggets

On this week’s podcast, Wes Reisz talks to Emmanuel Ameisen, head of AI for Insight Data Science, about building a semantic search system for images using convolution neural networks and word embeddings, how you can build on the work done by companies like Google, and then explores where the gaps are and where you need to train your own models.  The podcast wraps up with a discussion around how you get something like this into production.

Why listen to this podcast:

 - A common use case is the ability to search for similar things - I want to find another pair of sunglasses like these, or I want a cat that looks like this picture, or even a tool like Google’s Smart Reply, can all be considered broadly the domain of semantic search.

 - For image classification you generally want a convolutional neural network. You typically use a model pre-trained with a public data set like Imagenet pre-trained to generate embeddings, using the pre-trained model up to the penultimate layer, and storing the value of the activations.

 - From here the idea is to mix image embeddings with word embeddings.  The embeddings, whether for words or images, are just a vector that represents a thing.  There are many approaches to getting vectors for words, but the one that started it is word2vec.

 - For both image embeddings and word embeddings you can typically use pre-trained models, meaning that you only need to train the final step of bringing the two models together.

 - Before deploying to production it is important that you validate the model against biases such as sexism, typically using outside people to a carry out a through audit.

More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2RAEUrV

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq

Subscribe: www.youtube.com/infoq
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Check the landing page on InfoQ: https://bit.ly/2RAEUrV

Show Notes

Emmanuel Ameisen, Head of AI at Insight, on Building a Semantic Search System for Images

In this episode of the SuperDataScience Podcast, I chat with the Data Science Influencer and Data Science Author, Kate Strachnyi. You will hear about Kate's journey to becoming a data science influencer, know how she is massively contributing to the data science community,  and get insights how to create a culture of data science within an organization.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/205

SDS 205: Contributing to the community as a Data Science Influencer

Software engineers, architects and team leads have found inspiration to drive change and innovation in their team by listening to the weekly InfoQ Podcast. They have received essential information that helped them validate their software development map. We have achieved that by interviewing some of the top CTOs, engineers and technology directors from companies like Uber, Netflix and more. Over 1,200,000 downloads in the last 3 years.

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

With Ai X Podcast, Open Data Science Conference (ODSC) brings its vast experience in building community and its knowledge of the data science and AI fields to the podcast platform. The interests and challenges of the data science community are wide ranging. To reflect this Ai X Podcast will offer a similarly wide range of content, from one-on-one interviews with leading experts, to career talks, to educational interviews, to profiles of AI Startup Founders. Join us every two weeks to discover what’s going on in the data science community.

Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here - https://aiplus.training/  
Don't miss out on this exciting opportunity to expand your knowledge and stay ahead of the curve.

Having started DJing at 15, music has always been a passion. Now well into my 40s I spend any spare time I can get creating music solely on the iPad. Please leave feedback and don't forget to click like and repost if you do…

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