insideAI News, a Global Women in Data Science (WiDS) Conference media collaborator, is pleased to present the WiDS Stanford livestream. The Global WiDS Conference aims to inspire and educate data scientists worldwide, regardless of gender, and support women in the field. This annual one-day technical conference features outstanding women doing outstanding work.
Podcast Highlights: O’Reilly Data Show
March 1, 2020 by
Today’s feature podcast series comes from venerable O’Reilly Media that brings us compelling books (with different animals on the cover) on data science, programming, AI, machine learning, etc. The O’Reilly Data Show explores the opportunities and methodologies propelling data science, big data, and AI.
Podcast Highlights: Towards Data Science
February 23, 2020 by
Today’s feature podcast series comes from one my favorite data science blogs, Towards Data Science, which has consistently high-quality content to expand your knowledge of the field. Use the link to browse the 21 podcast series. Enjoy!
Data Science at Microsoft – Interviews with Practitioners
July 13, 2018 by
In this technical brief I wanted to pass along some great resources in support how data scientists approach their profession and illustrate the kind of background a typical data scientist might have to become successful. insideAI News previously featured four compelling podcast interviews with Microsoft data scientists.
Interview: Matt Winkler, Group Program Manager for Machine Learning at Microsoft
June 1, 2017 by
In this podcast interview, we caught up with Matt Winkler, Group Program Manager for Machine Learning at Microsoft, to get his take on the upward trajectory of data science, machine learning and the cloud – specifically Azure. Matt leads a team crafting tools and services to enable data scientists and developers to do more with their data. Originally from St. Louis, Matt has been at Microsoft for 11 years working on developer tools and cloud services such as the .NET Framework, Visual Studio, Azure Websites, Data Lake and HDInsight.
Interview: Jennifer Marsman, Principal Software Development Engineer at Microsoft
May 10, 2017 by
In this podcast interview, I caught up with Jennifer Marsman, Principal Software Development Engineer at Microsoft, to find out what it’s like to be a data scientist at Microsoft and get her take on the upward trajectory of AI and deep learning that we’re seeing in the industry today.
Interview: Dr. Danielle Dean, Senior Data Scientist Lead at Microsoft
May 4, 2017 by
In this podcast interview, I caught up with Dr. Danielle Dean, Senior Data Scientist Lead at Microsoft in the Algorithms and Data Science Group within the Cloud and Enterprise Division, to find out about her experience at Microsoft and get her take on the upward trajectory of AI and deep learning that we’re seeing in the industry today.
Interview: Anusua Trivedi, Data Scientist on Microsoft’s Advanced Data Science & Strategic Initiatives Team
April 13, 2017 by
In this podcast interview, I caught up with Anusua Trivedi, a Data Scientist on Microsoft’s ADS team, to get her take on the upward trajectory of AI and deep learning that we’re seeing in the industry today.
Slidecast: Announcing the Nvidia Deep Learning SDK
June 27, 2016 by
In this slidecast, Marc Hamilton from Nvidia describes the latest updates to the company’s Deep Learning Platform. “Great hardware needs great software. To help data scientists and developers make the most of the vast opportunities in deep learning, we’re announcing today at the International Supercomputing show, ISC16, a trio of new capabilities for our deep learning software platform. The three — NVIDIA DIGITS 4, CUDA Deep Neural Network Library (cuDNN) 5.1 and the new GPU Inference Engine (GIE) — are powerful tools that make it even easier to create solutions on our platform.”
The Hadooponomics Podcast Series
April 7, 2016 by
The Hadooponomics Podcast series is a creation of Blue Hill Research and hosted by James Haight, a principal analyst at the company. The mission of the Hadooponomics Podcast series is simple: to advance the Big Data conversation beyond its traditional buzz-heavy roots, and to take a look at the real story of what drives the value of Big Data.
