Solving for Productivity in Data Science with Modin and AI Optimizations

Image for FaceBook

 
Share this post:
Facebook | Twitter | Google+ | LinkedIn | Pinterest | Reddit | Email
 
This post can be linked to directly with the following short URL:


 
The audio player code can be copied in different sizes:
144p, 240p, 360p, 480p, 540p, Other


 
The audio player code can be used without the image as follows:


 
This audio file can be linked to by copying the following URL:


 
Right/Ctrl-click to download the audio file.
 
Subscribe:
Connected Social Media - iTunes | Spotify | Google | Stitcher | TuneIn | Twitter | RSS Feed | Email
Intel - iTunes | Spotify | RSS Feed | Email
Code Together - iTunes | Spotify | Google | Stitcher | SoundCloud | RSS Feed | Email
 

The rapid advancement in machine learning and data science fields have aided data scientists in arriving at meaningful insights. However, it’s not been an easy task to optimize machine learning infrastructures to allow data scientists to focus on their core expertise. Today, we will discuss how certain tools and hardware optimizations are not only saving time, but also enabling data scientists to be more productive.

Learn more:
Ponder.io
Intel oneAPI AI Analytics Toolkit
Intel Distribution for Python
Intel Distribution of Modin
Intel Extension for Scikit-learn
Intel Optimization for PyTorch
Intel Optimization for TensorFlow

Guests:
Devin Petersohn, Cofounder and CTO of Ponder
Areg Melik-Adamyan, Principal Engineer and Engineering Manager at Intel

Transcript Read/Download the transcript.
 

Tags: , , , , , , , , , , , , , , ,
 
Posted in: Audio Podcast, Code Together, Intel