Achieve High-Performance Scaling for E2E Machine Learning and Data Analytics Workflows

WTH is Pandas?

Well, they’re super cute tuxedo-colored bears from China who have six toes and eat bamboo.

Also … Pandas is dataframe library that allows to you perform data manipulation in Python, and it’s great for heterogeneous data—think data science—because it provides easy-to-use APIs to manipulate and process dataframes.

(You knew we’d get there.)

But there’s an issue: When working with excessively large amounts of data or when needing high-performance, single-core Pandas becomes a bottleneck for a data practitioner’s workflow. As a result, adopting a Pandas-workflow-compatible distributed system/solution is often needed.

This webinar discusses precisely that: Intel® Distribution of Modin*.

Join software engineer Areg Melik-Adamyan for a tour of this Distribution, including:

  • An overview Modin, including its OmniSci (accelerated analytics) backend
  • How to get the best performance and scaling through Intel Distribution of Modin
  • How to efficiently run end-to-end machine learning workloads without any code changes

Get the software

  • Download the Intel® Distribution of Modin* as part of the Intel® oneAPI AI Analytics Toolkit. Powered by oneAPI, the AI Kit includes 6 dev tools for accelerating data science and AI pipelines.
  • Sign up for an Intel® DevCloud for oneAPI account—a free development sandbox with access to the latest Intel® hardware and oneAPI software, including the AI Toolkit.

Other resources

Areg Melik-Adamyan, Software Engineering Manager, Intel Corporation

Areg Melik-Adamyan is an Engineering Manager and Architect with over 20 years’ experience in cross-architecture software development. Joining Intel in 2011, he currently works on next-gen data analytics, both software and its deployment across heterogeneous platforms. Areg holds a PhD in Computer Science from the Russian Academy of Sciences in Moscow and an MSc from Yerevan State University, Armenia.

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