Tools for Deploying Data Models into Production

Today's guest is mighty Erik Bernhardsson — creator of Spotify's music recommender, prolific open-source developer, world-leading technical blogger, and now model-deployment-tool entrepreneur via Modal Labs.

Erik:
• Is the Founder and CEO of Modal Labs, a startup building innovative tools and infrastructure for data teams.
• Previously was CTO of the real estate startup Better, where he grew the engineering team from the size of 1 — himself — to 300 people.
• Was also previously an Engineering Manager at Spotify, where he created their now-ubiquitous music-recommendation algorithm.
• Is a prolific open-sourcer, having created the popular Luigi and Annoy libraries, among several others.
• Is an industry-leading blogger with posts that frequently feature on the front page of Hacker News.

Today’s episode gets deep into the weeds at points, so it will be particularly appealing to practicing data scientists, ML engineers, and the like, but much of the fascinating, wide-ranging conversation in this episode will appeal to any curious listener.

In this episode, Erik details:
• How the Spotify music recommender he built works so well at scale.
•The litany of new data science and engineering tools he’s excited about and thinks you should be excited about too.
•What open-source library he would develop next.
•Why he founded his Modal and how their tools empower data teams.
• Having interviewed more than 2000 candidates for engineering roles, his top tips both for succeeding as an interviewer and as an interviewee.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Causality in Sequential Data

Inferring Causality is uniquely powerful when done with Sequential Data: data unfolding over time. Forecasting guru Dr. Sean Taylor — renowned for Prophet and now Motif Analytics co-founder — leads us through the topic.

Sean:
• Is Co-Founder and Chief Scientist of Motif Analytics, a startup that blends his deep expertise in causal modeling with sequential analytics.
• Previously worked as a Data Science Manager at Lyft.
• Also worked as a Research Scientist Manager at Facebook, where he led the development of the renowned open-source forecasting tool, Prophet.
• Holds a PhD in Information Systems from New York University and a BS in Economics from the University of Pennsylvania.

Today’s episode gets deep into the weeds on occasion, particularly when discussing making causal inferences, but most of the episode will resonate with any curious listener.

In this episode, Sean:
• Publicly unveils his new venture, filling us in on why now was the right time for him to co-found and lead data science at an ML startup.
• Details what causal modeling is, why every data scientist should be familiar with it, and how it can make a real-world impact, with many illustrative examples from his time at Lyft.
• Fills us in on the infrastructure and teams required for large-scale causal experimentation.
• Covers how causal modeling and forecasting can’t be fully automated today as it requires humans to make assumptions, but also how humans can make these assumptions in a more informed manner thanks to data visualizations.
• Explains what the field of Information Systems is and, having conducted several hundred interviews, what he looks for in the data scientists he hires.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

TEDx Talk: How Neuroscience Inspires A.I. Breakthroughs that will Change the World

My first TED-format talk is live! In it, I use (A.I.-generated!) visuals to color how A.I. will transform the world in our lifetimes, with particular emphases on climate change, food security, and healthcare innovations.

Thanks to Christina, Banu, and everyone at TEDxDrexelU for inviting me to speak, organizing a slick event, and masterfully editing the footage of my talk.

Thanks to Ed, Andrew, and Shaan at Nebula.io for providing invaluable feedback on drafts of my talk. It's only due to your constructive criticism that the final version turned out as well as it did. Thanks as well to Steven and Alex at Wynden Stark for kindly covering the travel costs of any employees that came down to Philadelphia to see the talk in-person.

Finally, thanks to Taya and Hannah at OpenAI for providing me with early access to custom images from their DALL-E 2 model. These were critical to me being able to tell the effectively convey the narrative I yearned to.

Data Science Interviews with Nick Singh

For an episode all about tips for crushing interviews for Data Scientist roles, our guest is Nick Singh — author of the bestselling "Ace the Data Science Interview" book and creator of the DataLemur SQL interview platform.

Nick:
• Co-authored “Ace the Data Science Interview”, an interview-question guide that has sold over 16,000 copies since it was released last year.
• Created the DataLemur platform for interactively practicing interview questions involving SQL queries.
• Worked as a software engineer at Facebook, Google, and Microsoft.
• Holds a BS in engineering from the University of Virginia.

Today's episode is ideal for folks who are looking to land a data science job for the first time, level-up into a more senior data science role, or perhaps land a data science gig at a new firm.

In this episode, Nick details:
• His top tips for success in data science interviews.
• Common misconceptions about data science interviews.
• How to become comfortable with self-promotion and increase your chances of landing your dream job.
• Strategies for when interviewers ask if you have any questions for them.
• The subject areas and skills you should master before heading into a data science interview.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Causal Machine Learning

Causal ML is today's focus with Dr. Emre Kiciman — Senior Principal Researcher at Microsoft, developer of the DoWhy causal modeling library for Python, and a leader in applying causal research to social sciences.

Emre:
• Has worked within prestigious Microsoft Research for over 17 years.
• Leads Microsoft’s research on Causal Machine Learning.
• Leads development of the DoWhy open-source causal modeling library for Python (part of the PyWhy GitHub project).
• Pioneered the use of social media data to answer causal questions in the social sciences, such as with respect to physical and mental health.
• Has published 100+ papers and been cited 8000+ times.
• Holds a PhD in Computer Science from Stanford University.

Today’s episode is relatively technical, so will probably appeal primarily to folks with technical backgrounds like data scientists, ML engineers, and software developers.

In this episode, Emre details:
• What Causal ML is and how it’s different from "correlational" ML.
• The four key steps of causal inference and how they impact ML.
• The types of data that are most amenable to causal methods and those that aren’t yet… but may be soon.
• Exciting real-world applications of Causal ML.
• The software tools he most highly recommends.
• What he looks for in the data science researchers he hires.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Open-Ended A.I.: Practical Applications for Humans and Machines

In today's remarkable episode, Dr. Kenneth Stanley uses evidence from his machine learning research on Open-Ended A.I. and evolutionary algorithms to inform how you as a human can achieve great life outcomes.

Ken:
• Co-authored the book "Why Greatness Cannot be Planned", a genre-defying book that leverages his ML research to redefine how a human can optimally achieve extraordinary outcomes over the course of their lifetime.
• Was until recently Open-Endedness Team Leader at OpenAI, one of the world’s top A.I. research organizations.
• Led Core A.I. Research for Uber A.I.
• With Prof. Gary Marcus and others, founded A.I. startup Geometric Intelligence, which was acquired by Uber.
• Was Professor of Computer Science at the University of Central Florida.
• Holds a dozen patents for ML innovations, including open-ended and evolutionary (especially neuroevolutionary) approaches.

Today’s episode does get fairly deep into the weeds of ML theory at points so may be best-suited to technical practitioners. That said, the broad strokes of the episode could be not only informative but, again, could indeed be life-perspective-altering for any curious listener.

In this episode, Ken details:
• What genetic ML algos are and how they work effectively in practice.
• How the Objective Paradox — that you fail to achieve an objective you seek — is common across ML and human pursuits.
• How an approach called Novelty Search can lead to superior outcomes than pursuing an explicit objective, again both for machines and humans.
• What Open-Ended A.I. is and its intimate relationship with AGI, a machine with the same learning potential as a human.
• His vision for how A.I. could transform life for humans in the coming decades.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Data Mesh

"Data Mesh" may be the trendiest term in data science. What is it and how will its Distributed A.I. transform your organization? The founder of the Data Mesh concept herself, Zhamak Dehghani, explains in this episode.

Zhamak:
• Authored the O'Reilly Media book "Data Mesh" and also co-authored an O’Reilly book on software architecture.
• Is newly the CEO and founder of a stealth tech startup reimagining the future of the data developer experience though the Data Mesh.
• Previously worked as a software engineer, software architect, and as a technology incubation director.
• Holds a Bachelor of Engineering degree in Computer Software from the Shahid Beheshti University in Iran and a Masters in Information Technology Management from the University of Sydney in Australia.

Today’s episode should be broadly interesting to anyone who’s keen to get a glimpse of the future of how organizations will work with data and A.I.

In this episode, Zhamak details:
• What a data mesh is.
• Why data meshes are essential today and will be even more so in the coming years.
• The biggest challenges of distributed data architectures.
• Why now was the right time for her to launch her own data mesh startup.
• Her tricks for keeping pace with the rapid of pace of tech progress.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Inferring Causality with Jennifer Hill

Inferring causal direction — as opposed to merely identifying correlations — is central to all real-world data science applications. World-leading expert and author on causality, Prof. Jennifer Hill, is our guest this week.

Jennifer:
• Is Professor of Applied Statistics at New York University, where she researches causality and practical applications of causal research, such as those that are vital to scientific development and government policies.
• Co-directs the NYU Masters in Applied Statistics and directs PRIISM (a center focused on impactful social applications of data science).
• With the renowned statistician Andrew Gelman, wrote the book "Data analysis using regression and multilevel/hierarchical models", an iconic textbook that has been cited over 15k times.
• Holds a PhD in Statistics from Harvard University.

Intended audience:
• Today’s episode largely contains content that will be of interest to anyone who’s keen to better understand the critical concept of causality.
• It also contains technical parts that will appeal primarily to practicing data scientists.

In this episode, Jennifer details:
• How causality is central to all applications of data science.
• How correlation does not imply causation.
• How to design research in order to confidently infer causality from the results.
• Her favorite Bayesian and machine learning tools for making causal inferences within code.
• ThinkCausal, her new graphical user interface for making causal inferences without the need to write code.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Upskilling in Data Science and Machine Learning

This week, iconic Stanford University Deep Learning instructor and entrepreneur Kian Katanforoosh details how ML powers his EdTech platform Workera, enabling you to systematically fill gaps in your data science skills.

Kian:
• Is Co-Founder and CEO of Workera, a Bay Area education technology company that has raised $21m in venture capital to upskill workers, with a particular early focus on upskilling technologists like data scientists, software developers, and machine learning specialists.
• Is a lecturer of computer science at Stanford University (specifically, he teaches the extremely popular CS230 Deep Learning course alongside Prof. Andrew Ng, one of the world’s best-known data scientists).
• Was awarded Stanford’s highest teaching award.
• Is also a founding member of DeepLearning.AI, a platform through which he’s taught over three million students deep learning.
• Holds a Masters in Math and Computer Science from CentraleSupélec.
• Holds a Masters in Management Science and Engineering from Stanford.

By and large, today’s episode will appeal to any listener who’s keen to understand the latest in education technology, but there are parts here and there that will specifically appeal to practicing technologists like data scientists and software developers.

In this episode, Kian details:
• What a skills intelligence platform is.
• Four ways that machine learning drives his skills intelligence platform.
• What frameworks and software languages they selected for building their platform and why.
• What he looks for in the data scientists and software engineers he hires.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Geospatial Data and Unconventional Routes into Data Careers

This week, the remarkably well-read Christina Stathopoulos, details open-source software for working with geospatial data... as well as how you can navigate your data-career path, no matter what your background.

Christina:
• Has worked at Google for nearly five years in several data-centric roles.
• For the past year, she’s worked as an Analytical Lead for Waze, the popular crowdsourced navigation app owned by Google.
• Is also an adjunct professor at IE Business School School in Madrid, where she teaches courses on business analytics, machine learning, data visualization, and data ethics.
• Previously worked as a data engineer at media analytics giant Nielsen.
• Holds a Master’s in Business Analytics and Big Data from IE Business School and a Bachelor’s in Science, Tech, and Society from North Carolina State University.

Today’s episode will appeal to a broad audience of technical and non-technical listeners alike.

In this episode, Christina details:
• Geospatial data and open-source packages for working with it.
• Her tips for getting a foothold in a data career if you come from an unconventional background.
• Guidance to help women and other underrepresented groups thrive in tech.
• The hard and soft skills most essential to success in a data role today.
• Her #bookaweekchallenge and her top data book recommendations.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Venture Capital for Data Science

Keen to get the inside scoop on the Venture Capital industry and tech startup investing? Sarah Catanzaro, an eloquent VC who specializes in growing the value of Data Science companies, is our guest this week.

Sarah:
• Is a General Partner at Amplify Partners, a Bay Area venture capital firm that specializes in investing in early-stage start-ups that are pioneering new applications of data science, analytics, and machine learning.
• Previously she worked as an investor at Canvas Ventures, as Head of Data at Mattermark, and as an embedded analyst at Palantir.
• She holds a Bachelor of Science degree from Stanford University.

Today’s episode will appeal to anyone who’s keen to understand investing in early-stage start-ups.

In this episode, Sarah details:
• What venture capital is and how it differs from private equity investment.
• How to go from a data science idea to obtaining funding.
• How to pick winning investments.
• What start-ups can do to survive or raise capital in the current economic climate.
• The lessons she’s learned from ten years of experience in the field of data science.
• How to break into the field of venture capital yourself.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

MLOps: Machine Learning

Analogous to the role DevOps plays for software development, MLOps enables efficient ML training and deployment. MLOps expert Mikiko Bazeley is our guide!

Mikiko:
• Is a Senior Software Engineer responsible for MLOps at Intuit Mailchimp.
• Previously held technical roles at a range of Bay Area startups, with responsibilities including software engineering, MLOps, data engineering, data science, and data analytics.
• Is a prominent content creator on MLOps – across live workshops, her YouTube channel, her personal blog, and the NVIDIA blog.

Today’s episode will appeal primarily to hands-on practitioners such as data scientists and software engineers.

In this episode, Mikiko details:
• What MLOps is.
• Why MLOps is critical for the efficiency of any data science team.
• The three most important MLOps tools.
• The four myths holding people back from MLOps expertise.
• The six most essential MLOps skills for data scientists.
• Her productivity tricks for balancing software engineering, content creation, and her athletic pursuits.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Getting Kids Excited about STEM Subjects

For the fourth and final Friday episode featuring the inimitable Ben Taylor, he provides guidance on how to get kids excited about STEM (science, tech, engineering, math) subjects.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

A.I. Policy at OpenAI

OpenAI released many of the most revolutionary A.I. models of recent years, e.g., DALL-E 2, GPT-3 and Codex. Dr. Miles Brundage was behind the A.I. Policy considerations associated with each transformative release.

Miles:
• Is Head of Policy Research at OpenAI.
• He’s been integral to the rollout of OpenAI’s game-changing models such as the GPT series, DALL-E series, Codex, and CLIP.
• Previously he worked as an A.I. Policy Research Fellow at the University of Oxford’s Future of Humanity Institute.
• He holds a PhD in the Human and Social Dimensions of Science and Technology from Arizona State University.

Today’s episode should be deeply interesting to technical experts and non-technical folks alike.

In this episode, Miles details:
• Considerations you should take into account when rolling out any A.I. model into production.
• Specific considerations OpenAI concerned themselves with when rolling out:
• The GPT-3 natural-language-generation model,
• The mind-blowing DALL-E artistic-creativity models,
• Their software-writing Codex model, and
• Their bewilderingly label-light image-classification model CLIP.
• Differences between the related fields of AI Policy, AI Safety, and AI Alignment.
• His thoughts on the risks of AI displacing versus augmenting humans in the coming decades.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The A.I. Platforms of the Future

Ben Taylor returns for a third consecutive Five-Minute Friday! This week, he helps us look ahead and dig into what we can expect from the A.I. platforms of the future.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Data Engineering 101

Today's episode is all about Data Engineering — particularly the tools and techniques that Data Scientists should know. "Fundamentals of Data Engineering" book co-authors Matthew Housley and Joe Reis are guests!

Matt and Joe:
• Co-authored the brand-new "Fundamentals of Data Engineering" book that was published by O'Reilly Media and is already a bestseller.
• Co-founded the data architecture and data engineering consultancy Ternary Data. Joe is CEO of the firm while Matt is CTO.

In addition, Joe:
• Is an adjunct professor at the University of Utah.
• Previously founded several tech companies and has held both software engineering and data science roles.
• Holds a math degree from the University of Utah.

Matt:
• Holds a PhD in math from the University of Utah.
• Worked as a professor before becoming a data scientist in industry.

Today’s episode will appeal primarily to technical experts like data scientists and data engineers, but will also be of interest to anyone who manages technology projects that involve data flows.

In this episode, Matt and Joe detail:
• Why they identify as “recovering data scientists”.
• What kinds of people tend to become data scientists versus what kinds tend to become data engineers.
• Key components of their book such as latency trade-offs and the six data engineering undercurrents.
• Their favorite data engineering tools and techniques.
• What the Live Data Stack is and how it’s putting various data professional titles on a collision course.
• The biggest data engineering problems firms face and how to fix them.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Why CEOs Care About A.I. More than Other Technologies

Ben Taylor is back for another Five-Minute Friday this week, this time to fill us in on why CEOs care more about A.I. than any other technology and how to sell them on your machine learning solution.

Special shout-out to my puppy Oboe who features indispensably in the video version of this episode... on Ben's lap! 🐶

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Real-World Impact of Cross-Disciplinary Data Science Collaboration

How to unlock breakthroughs — particularly in medicine — through cross-disciplinary data science is the main topic covered this week with the fascinating, trailblazing Professor Philip Bourne.

Philip:
• Is Founding Dean of the University of Virginia's School of Data Science.
• Is also Professor of Biomedical Engineering at Virginia.
• Is Founding Editor-in-Chief of the open-access journal PLOS Computational Biology.
• Was previously Associate Director for Data Science of The National Institutes of Health

Despite Prof. Bourne being a deep technical expert, he conveys concepts so magnificently that today’s episode should be broadly appealing to practicing data scientists and non-technical listeners alike.

In this episode, Philip details:
• Why he founded a School of Data Science.
• Why such schools are uniquely positioned to bear the fruits of applied data science research within universities.
• What the most important data science skills are.
• How computing and data science have evolved across academic departments in the recent decades.
• Fascinating practical applications of his biomedical data science research into the structure and function of biological proteins.
• The absolutely essential role of open-source software and open-access publishing in data science.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.