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

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Tags: AI Artificial intelligence, API, Data Science, data scientist, domain specific languages, heterogeneous, machine learning, Modin, OmniSci, oneAPI, Opensource, Pandas, Python, Pytorch, SQL, XGBoost
 
Posted in: Audio Podcast, Code Together, Intel