Numerically Speaking: The Anaconda Podcast
How are data and next-generation computing technologies transforming our world? Who are the inventors, the business leaders, and the rebels and scientists at the heart of the AI revolution? On Numerically Speaking, we connect with guests from around the world to help you learn what's new, what's good, and what’s next.
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Data Engineering as a Scientific Tool
01/11/2023
Data Engineering as a Scientific Tool
In this episode, host is joined by Dr. Patrick Kavanagh, an astrophysicist and software developer at the . Patrick works on the (JWST), helping to write code that allows scientists to interpret the raw data they receive from space. Patrick talks to Peter about cleaning telescope data sets to make them more scientifically useful, and more. Patrick’s team working on the Mid-Infrared Instrument on the JWST writes software in Python to help deliver science-ready data to astronomers and astrophysicists. Patrick’s work facilitates more precise study of distant stars and galaxies in a way that fosters public trust. Peter Wang - Dublin Institute for Advanced Studies - James Webb Space Telescope - Check out these relevant resources: If you enjoyed today’s show, please leave a 5-star review. For more information, visit . #Computing #AI #Data #DataScience #Analytics
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Optimizing Python for Speed and Compatibility
12/28/2022
Optimizing Python for Speed and Compatibility
In the penultimate episode of season one, host and , Software Engineer at (owned by Meta), discuss considerations around making Python faster while maximizing compatibility and performance. Several years ago, Carl and his team started working on a project called in an effort to improve CPU efficiency across Meta’s servers by “[optimizing] things at the level of Python runtime.” While initially meant to serve as a stop gap, Cinder yielded impressive wins that transformed it into a premier and ongoing project at Instagram. In addition to Cinder, Peter and Carl discuss: - Carl’s experiences with various programming languages like TI-Basic, Perl, and PHP - Challenges around innovating on an established language with 30+ years of history - The potential evolution of Python use cases and best practices - And more! Peter Wang - Carl Meyer - Instagram - Cinder - If you enjoyed today’s show, please leave a 5-star review. For more information, visit . #Computing #AI #Data #DataScience #Analytics
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Climate Science, Scientific Computing, and Data Accessibility
12/14/2022
Climate Science, Scientific Computing, and Data Accessibility
This episode’s conversation between host Peter Wang and , Associate Professor at , explores climate science, scientific computing, data accessibility, and more. Topics that Peter and Ryan cover include: - Cloud computing - Open data and collaboration - Climate science and the private sector - Open-source projects like and Climate data is sometimes restricted in the way it flows between interested parties; the growth of private industry around data storage and dissemination has put up barriers to entry that can limit access to valuable systems and data. This is especially troubling to Ryan because these barriers often exclude some of the people who are most affected by climate change. He feels that usable information can and should be made accessible without undermining private interests. Peter Wang - Ryan Abernathey - Columbia University in the City of New York - Pangeo Forge - Xarray - You can find a human-verified transcript of this episode . - If you enjoyed today’s show, please leave a 5-star review. For more information, visit .
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Shaping Best Practices for Monitoring ML Models
11/30/2022
Shaping Best Practices for Monitoring ML Models
In this episode, host is joined by , CEO and Co-Founder of . Peter and Elena discuss how Evidently AI’s open-source tooling is helping users monitor machine learning (ML) models, and why that’s important. Elena has found that Evidently AI’s open-source approach is attractive to data scientists and ML engineers who are ramping up model maintenance, retraining, and monitoring efforts. Peter and Elena also touch on: - On-premises versus cloud-based deployment - ML model monitoring best practices - The value of pipeline testing - And more! You can find a human-verified transcript of this episode . - https://know.anaconda.com/rs/387-XNW-688/images/ANACON_%20Elena%20Samuylova_%20HVT.docx.pdf If you enjoyed today’s show, please leave a 5-star review. For more information, visit . #ML #AI #Data #DataScience #Analytics
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Unifying and Accelerating Data Science, ML, and Advanced Analytics Workflows
11/16/2022
Unifying and Accelerating Data Science, ML, and Advanced Analytics Workflows
In this episode, host speaks with , Director of Product Management at , about how Snowflake solutions support professionals in data science, machine learning, and advanced analytics. Torsten has worked with data throughout his entire career. At Snowflake, he focuses on Snowflake's data lake, data pipelines, and data science workloads, as well as Snowflake's developer and partner ecosystem. Thanks to the broader language compatibilities of Snowflake and its Snowpark library, data engineering is becoming more accessible beyond the SQL community. Torsten and Snowflake continue to work to unify and accelerate data workflows. Peter Wang - https://www.linkedin.com/in/pzwang/ Tosten Grabs - Snowflake - Learn more about , - https://www.snowflake.com/snowpark/ now , - and get started with the . - Then, dive into the - and learn . https://www.snowflake.com/blog/snowflake-partners-with-and-invests-in-anaconda-to-bring-enterprise-grade-open-source-python-innovation-to-the-data-cloud/ Access Anaconda’s State of Data Science report, referenced by Peter, . - You can find a human-verified transcript of this episode . - If you enjoyed today’s show, please leave a 5-star review. For more information, visit .
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Autopoiesis in Systems of People and Machines
11/02/2022
Autopoiesis in Systems of People and Machines
In “Autopoiesis in Systems of People and Machines,” welcomes . Paco is a Managing Partner at , 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. Peter and Paco discuss histories and frameworks that are impacting today’s systems of people and machines. Paco touches on corporate law and how long ago, the concept of insurance allowed for the externalization of risk and corresponding enablement of capital ventures. Paco goes on to talk about autopoiesis, the Chilean Project Cybersyn and the significance of groupware, and the core of human intelligence. Peter and Paco also discuss the increasing complexity of today’s world in which less and less is linear, which requires improved cognition for survival, and the cybernetic future. Resources: “” (David M. Holland) - https://www.soa.org/globalassets/assets/library/newsletters/reinsurance-section-news/2009/february/rsn-2009-iss65-holland.pdf - https://supreme.justia.com/cases/federal/us/118/394/ “” (Gunther Teubner) - https://cadmus.eui.eu/handle/1814/23894 (Humberto Maturana and Francisco Varela) - https://en.wikipedia.org/wiki/Autopoiesis_and_Cognition:_The_Realization_of_the_Living - https://99percentinvisible.org/episode/project-cybersyn/ “” (Terry Winograd and Fernando Flores) - https://philpapers.org/rec/WINUCA - https://en.wikipedia.org/wiki/Macy_conferences - https://en.wikipedia.org/wiki/Norbert_Wiener “” (J.Y. Lettvin et al.) - https://hearingbrain.org/docs/letvin_ieee_1959.pdf - - https://en.wikipedia.org/wiki/Niklas_Luhmann (Paul Pangaro) (When Paco references Donoho Design, he means Dubberly Design.) - http://www.dubberly.com/articles/cybernetics-and-design.html - https://en.wikipedia.org/wiki/Ren%C3%A9_Thom “” (Paco Nathan) - https://www.tripzine.com/listing.php?id=corporate_metabolism You can find a human-verified transcript of this episode - https://know.anaconda.com/rs/387-XNW-688/images/ANACON_Paco_Nathan_V1.docx.pdf If you enjoyed today’s show, please leave a 5-star review. For more information, visit Anaconda.com/podcast.
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From “Enthusiastic User” to pandas Maintainer
10/19/2022
From “Enthusiastic User” to pandas Maintainer
On this episode of Numerically Speaking: The Anaconda Podcast, host welcomes pandas maintainer , Managing Director at . 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. When it comes to open source, says Peter, “my flame isn’t diminished by lighting your candle.” Cloning a copy of pandas, for example, does not make the original copy any less valuable. In fact, source code actually increases in value as it circulates. Until recently, only volunteers worked on pandas—but as of 2022, three full-time maintainers are paid to contribute, review code, and triage issues. Jeff’s advice for anybody interested in contributing to open source? Find a community and just help out. Click to check out “Two Sigma Presents Pandas at a Crossroads the Past Present and Future with Jeff Reback” on YouTube. You can find a human-verified transcript of this episode - https://know.anaconda.com/rs/387-XNW-688/images/ANACON_Jeff%20Reback_V1.docx.pdf Resources: Peter Wang LinkedIn - Jeff Reback LinkedIn - Two Sigma LinkedIn - If you enjoyed today’s show, please leave a 5-star review. For more information, visit .
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A Specialized Approach to Hardware
10/05/2022
A Specialized Approach to Hardware
End users who are not schooled in hardware can often default to, “just give me something that works.” , Staff AI Engineer, Strategy & Vision for Data Science and AI Products at , 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. David and host also discuss: - The need for more education and resources around hardware performance - Intel’s Optane technology and the possibilities it creates Resources: Peter Wang LinkedIn - David Liu LinkedIn - Intel LinkedIn - Click to visit David’s YouTube channel. You can find a human-verified transcript of this episode - . If you enjoyed today’s show, please leave a 5-star review. For more information, visit .
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Human in the Loop
09/21/2022
Human in the Loop
Machine learning (ML) has reached an exciting phase of development, a phase that , Senior ML Engineer at * 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. Vicki’s discussion with host covers: The interplay between software engineering and ML, the human element of the development lifecycle (and the lack thereof in social media) and the operationalization and the rise of microservices. Resources: Click to visit Vicki’s blog. Click to purchase The Presentation of Self in Everyday Life by Erving Goffman, referenced by Vicki. Click to purchase Broad Band: The Untold Story of the Women Who Made the Internet, also referenced by Vicki. Click to listen to the Jim Rutt/Rob Malda (Slashdot) podcast episode referenced by Peter. Check out the P2 website You can find a human-verified transcript of this episode here - . If you enjoyed today’s show, please leave a 5-star review. For more information, visit. *At the time of the interview, Vicki Boykis was an ML Engineer working on Tumblr at .
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Software, Venture Capital, and the Future of Work
09/07/2022
Software, Venture Capital, and the Future of Work
While today’s software may seem magical compared to that of previous generations, it still takes multiple software iterations to fold in new fundamental technologies. Joining us for this episode is , Partner at . Bloomberg Beta runs several seed-stage investment funds, with a particular interest in low-code/no-code/WebAssembly startups. In this episode, James and host discuss: - why it’s important to be humble when looking towards the future of software - why venture capitalists (VCs) shouldn’t be considered “Yodas” who can fix every problem - what James is looking for when it comes to investing in a business After listening to this episode, you may enjoy reading “ - ” by John Perry Barlow, referenced by Peter during the discussion, and “ - ” and “ - ,” referenced by James. You can find a human-verified transcript of this episode - . Resources: Peter Wang LinkedIn - James Cham LinkedIn - Bloomberg Beta LinkedIn - If you enjoyed today’s show, please leave a 5-star review. For more information, visit .
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Introducing Numerically Speaking: The Anaconda Podcast
06/22/2022
Introducing Numerically Speaking: The Anaconda Podcast
In this introductory episode of Numerically Speaking: The Anaconda Podcast, Anaconda CEO Peter Wang provides an overview of what to expect from this podcast. Peter will be exploring a variety of topics within the dynamic world of data science, including quantitative computing, business, and entrepreneurship. Guests will include top data science experts as well as creators of cutting-edge open-source tools. Whether you want to learn about AI, grow your data science career, or simply better understand the numbers and computers that shape our world, this podcast is for you. We're excited to bring you insights about data science and the people that make it happen. Be sure to subscribe to stay up to date with new episodes. This episode is brought to you by Anaconda, the world's most popular data science platform. We are committed to increasing data literacy and providing data science technology for a better world. Anaconda is the best way to get started with, deploy, and secure Python data science software. If you enjoyed today’s show, please leave a 5-star review. For more information and links to the resources mentioned in this episode, please visit . - - - - - - - -
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