Episode 73: Datasets for Software 2.0 with Taivo Pungas

Timestamps(01:30) Taivo shared briefly about his experience going through the Estonian K-12 system, as argued in his blog post written in Estonian.(05:34) Taivo described his undergraduate experience studying Computer Science at the University of Tartu and exposing to Machine Learning.(08:15) Taivo discussed his time interning at Skype and TransferWise.(10:01) Taivo went over his Master's Degree in Computer Science at ETH Zurich, where he worked on a thesis called "Uncertainty-based active imitation learning" at the Learning and Adaptive Systems Group.(17:17) Taivo talked about his time working at Starship Technologies as a Perception Engineer.(21:26) Taivo unpacked the Data Specification Manifesto that entails 3 principles for iteratively solving complex problems.(27:21) Taivo unpacked "The Two Loops Of Building Algorithmic Products" from his experience at Veriff - an Estonian startup that develops an identity verification platform.(32:11) Taivo discussed how his team at Veriff developed automation-heavy products.(36:45) Taivo shared lessons learned as a Product Manager at Veriff: leading the go-to-market strategy, establishing communication between the product and sales division, and building a unique DataOps team that creates good datasets.(44:31) Taivo described the key characteristics and properties of a tool that can address the whole data annotation workflow (Read his article "Data Loops Are The Bottleneck In Applied AI").(49:33) Taivo predicted the evolution of the DataOps discipline for AI teams in the upcoming years (Read his article "Your AI Team Needs DataOps").(54:01) Taivo untangled the relationship between sampling and labeling, and their importance in the AI development process (Read his article "Datasets Carve The Terrain of AI").(56:36) Taivo talked about the tools that he's most excited about during the transition to Software 2.0.(01:00:04) Taivo shared his journey thus far as the founder of a stealth startup.(01:06:21) Taivo revealed insider insights about the #EstonianMafia startup ecosystem.(01:09:36) Taivo shared the productivity tips that have been most useful to his personal/professional growth.(01:14:10) Closing segment.Taivo's ContactWebsiteTwitterLinkedInMediumGoogle ScholarMentioned ContentBlog PostsData Specification Manifesto"Building Automation-Heavy Products" (April 2019)"Data Loops Are The Bottleneck In Applied AI" (June 2019)"Your AI Team Needs DataOps" (July 2020)"Datasets Carve The Terrain of AI" (Nov 2020)Talks"The Two Loops Of Building Algorithmic Products" (April 2019)"How To Build Your AI Startup" (June 2020)"Datasets: The Source Code of Software 2.0" (Nov 2020)PeopleAndrej Karpathy (The Senior Director of AI at Tesla, who coined the term Software 2.0)Mike Bostock (The Creator of D3.js)Book"Surely You're Joking, Mr. Feynman" (by Richard Feynman)About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing [email protected].

Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.