In the penultimate episode of season one, host Peter Wang and Carl Meyer, Software Engineer at Instagram (owned by Meta), discuss considerations around making Python faster while maximizing compatibility and performance.

This episode’s conversation between host Peter Wang and Ryan Abernathey, Associate Professor at Columbia University in the City of New York, explores climate science, scientific computing, data accessibility, and more.

In this episode, host Peter Wang is joined by Elena Samuylova, CEO and Co-Founder of Evidently AI. Peter and Elena discuss how Evidently AI’s open-source tooling is helping users monitor machine learning (ML) models, and why that’s important.

In this episode, host Peter Wang speaks with Torsten Grabs, Director of Product Management at Snowflake, about how Snowflake solutions support professionals in data science, machine learning, and advanced analytics.

In “Autopoiesis in Systems of People and Machines,” Peter Wang welcomes Paco Nathan. Paco is a Managing Partner at Derwen, Inc., a company that offers enterprise customers full-stack engineering for AI applications at scale, with an emphasis on open-source integrations. Paco forged a career in artificial intelligence when many people were skeptical of it and now boasts over 40 years of computer science experience.

On this episode of Numerically Speaking: The Anaconda Podcast, host Peter Wang welcomes pandas maintainer Jeff Reback, Managing Director at Two Sigma.

Jeff began his career on Wall Street in the 1990’s and used Perl for a long time. He developed an interest in Python in the 2000’s. He was then quickly drawn to pandas and began to spend his hour-long ferry commutes contributing to its open-source code. His contributions over the years have been significant, to say the least.

End users who are not schooled in hardware can often default to, “just give me something that works.” David Liu, Staff AI Engineer, Strategy & Vision for Data Science and AI Products at Intel, understands this thinking but also believes that end users can be educated on the advantages of configuring their computer hardware to suit their specific needs.

David advocates for using the right hardware for a given task—and that may mean different configurations and/or different machines for different tasks, rather than a one-size-fits-all solution.

Machine learning (ML) has reached an exciting phase of development, a phase that Vicki Boykis, Senior ML Engineer at Duo Security* has characterized as the “steam-powered days.” In this episode of Numerically Speaking: The Anaconda Podcast, Vicki talks about the state of the industry and where she sees things heading.

*At the time of the interview, Vicki Boykis was an ML Engineer working on Tumblr at Automattic.

Joining us for this episode is James Cham, Partner at Bloomberg Beta. Bloomberg Beta runs several seed-stage investment funds, with a particular interest in low-code/no-code/WebAssembly startups.

In this introductory episode of Numerically Speaking: The Anaconda Podcast, Anaconda CEO Peter Wang provides an overview of what to expect from the show.