Numerically Speaking: The Anaconda Podcast

In this episode, host Peter Wang [https://www.linkedin.com/in/pzwang/] is joined by Dr. Patrick Kavanagh, an astrophysicist and software developer at the Dublin Institute for Advanced Studies [https://www.linkedin.com/school/dublin-institute-for-advanced-studies/]. Patrick works on the James Webb Space Telescope [https://webb.nasa.gov/] (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 - https://www.linkedin.com/in/pzwang/ [https://www.linkedin.com/in/pzwang/] Dublin Institute for Advanced Studies - https://www.linkedin.com/school/dublin-institute-for-advanced-studies/ [https://www.linkedin.com/school/dublin-institute-for-advanced-studies/] James Webb Space Telescope - https://webb.nasa.gov/ [https://webb.nasa.gov/]     Check out these relevant resources: * Dr. Patrick Kavanagh - EuroPython [https://ep2022.europython.eu/keynoters/patrick-kavanagh] * Python and James Webb [https://analyticsindiamag.com/the-breakthrough-nasas-image-of-the-universe-has-a-unique-python-angle/] * Judy Schmidt (citizen scientist) [https://explorersweb.com/james-webb-telescope-jupiter-images/#:~:text=Judy%20Schmidt%2C%20a%20Modesto%2C%20California,NASA's%20James%20Webb%20Space%20Telescope.]  If you enjoyed today’s show, please leave a 5-star review. For more information, visit anaconda.com/podcast [http://anaconda.com/podcast].     #Computing #AI #Data #DataScience #Analytics

In the penultimate episode of season one, host Peter Wang [https://www.linkedin.com/in/pzwang/] and Carl Meyer [https://www.linkedin.com/in/carljm/], Software Engineer at Instagram [https://www.linkedin.com/company/instagram/] (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 Cinder [https://github.com/facebookincubator/cinder] 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 - https://www.linkedin.com/in/pzwang/ [https://www.linkedin.com/in/pzwang/] Carl Meyer - https://www.linkedin.com/in/carljm/ [https://www.linkedin.com/in/carljm/] Instagram - https://www.linkedin.com/company/instagram/ [https://www.linkedin.com/company/instagram/] Cinder - https://github.com/facebookincubator/cinder [https://github.com/facebookincubator/cinder]   If you enjoyed today’s show, please leave a 5-star review. For more information, visit https://www.anaconda.com/podcast [https://www.anaconda.com/podcast].   #Computing #AI #Data #DataScience #Analytics

This episode’s conversation between host Peter Wang and Ryan Abernathey [https://www.linkedin.com/in/ryan-abernathey-32a70652/], Associate Professor at Columbia University in the City of New York [https://www.linkedin.com/school/columbia-university/], 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 Pangeo Forge [https://pangeo-forge.org/] and Xarray [https://docs.xarray.dev/en/stable/]   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 - https://www.linkedin.com/in/pzwang/ [https://www.linkedin.com/in/pzwang/] Ryan Abernathey - https://www.linkedin.com/in/ryan-abernathey-32a70652/ [https://www.linkedin.com/in/ryan-abernathey-32a70652/] Columbia University in the City of New York - https://www.linkedin.com/school/columbia-university/ [https://www.linkedin.com/school/columbia-university/] Pangeo Forge - https://pangeo-forge.org/ [https://pangeo-forge.org/] Xarray - https://docs.xarray.dev/en/stable/ [https://docs.xarray.dev/en/stable/]   You can find a human-verified transcript of this episode here [https://know.anaconda.com/rs/387-XNW-688/images/ANACON_%20Ryan%20Abernathey_HVT.docx.pdf]. - https://know.anaconda.com/rs/387-XNW-688/images/ANACON_%20Ryan%20Abernathey_HVT.docx.pdf [https://know.anaconda.com/rs/387-XNW-688/images/ANACON_%20Ryan%20Abernathey_HVT.docx.pdf]   If you enjoyed today’s show, please leave a 5-star review. For more information, visit https://www.anaconda.com/podcast [https://www.anaconda.com/podcast].

In this episode, host Peter Wang [https://www.linkedin.com/in/pzwang/] is joined by Elena Samuylova [https://www.linkedin.com/in/elenasamuylova/?originalSubdomain=uk], CEO and Co-Founder of Evidently AI [https://www.evidentlyai.com/]. 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 here [https://know.anaconda.com/rs/387-XNW-688/images/ANACON_%20Elena%20Samuylova_%20HVT.docx.pdf]. - 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 anaconda.com/podcast [http://anaconda.com/podcast].   #ML #AI #Data #DataScience #Analytics

In this episode, host Peter Wang [https://www.linkedin.com/in/pzwang/] speaks with Torsten Grabs [https://www.linkedin.com/in/torstengrabs/], Director of Product Management at Snowflake [https://www.linkedin.com/company/snowflake-computing/], 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 - https://www.linkedin.com/in/torstengrabs/ [https://www.linkedin.com/in/torstengrabs/] Snowflake - https://www.linkedin.com/company/snowflake-computing/ [https://www.linkedin.com/company/snowflake-computing/]   Learn more about Snowpark for Python [https://www.snowflake.com/snowpark/], - https://www.snowflake.com/snowpark/ now generally available [https://www.snowflake.com/news/snowflake-disrupts-application-development-with-general-availability-of-snowpark-for-python-native-streamlit-support-and-more/], - https://www.snowflake.com/news/snowflake-disrupts-application-development-with-general-availability-of-snowpark-for-python-native-streamlit-support-and-more/ [https://www.snowflake.com/news/snowflake-disrupts-application-development-with-general-availability-of-snowpark-for-python-native-streamlit-support-and-more/] and get started with the Snowpark Developer Guide for Python [https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html]. - https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html [https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html] Then, dive into the Snowflake-Anaconda partnership [https://www.snowflake.com/blog/snowflake-partners-with-and-invests-in-anaconda-to-bring-enterprise-grade-open-source-python-innovation-to-the-data-cloud/] - https://www.snowflake.com/blog/snowflake-partners-with-and-invests-in-anaconda-to-bring-enterprise-grade-open-source-python-innovation-to-the-data-cloud/ [https://www.snowflake.com/blog/snowflake-partners-with-and-invests-in-anaconda-to-bring-enterprise-grade-open-source-python-innovation-to-the-data-cloud/] and learn how Snowflake customers like Allegis Group are leveraging Snowpark for Python [https://www.youtube.com/watch?v=z6CDNLxT9ZQ&t=14s]. 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, here [https://www.anaconda.com/state-of-data-science-report-2022]. - https://www.anaconda.com/state-of-data-science-report-2022 [https://www.anaconda.com/state-of-data-science-report-2022]   You can find a human-verified transcript of this episode here [https://know.anaconda.com/rs/387-XNW-688/images/ANACON_%20Torsten%20Grabs_%20HVT.docx.pdf]. - https://know.anaconda.com/rs/387-XNW-688/images/ANACON_Paco_Nathan_V1.docx.pdf [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 [https://anaconda.com/podcast].