Intel® Optimization for TensorFlow*: Tips & Tricks for AI+HPC Convergence

This guided tutorial Introduces a key machine learning framework for optimizing AI inference workloads: the Intel® Optimization for Tensorflow*.

AI Technical Consulting Engineer Preethi Venkatesh discusses how developers can achieve different levels of optimizations and see performance benefits using this Intel-optimized tool. The session includes use-case demos, case studies, and benchmarks. She also answers questions posed by the audience.

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Preethi Venkatesh, Technical Consulting Engineer, Intel Corporation

Preethi is a Technical Consulting Engineer focused on helping customers use and adopt the Intel® Distribution for Python* and Intel® Data Analytics Acceleration Library through training, article publication, and open-source contributions. She joined Intel in 2017, coming from a 4-year tour at Infosys Limited where she was a Business Data Analyst.

Preethi has a bachelor’s degree in Instrumentation Technology from Visvesvaraya Technological University, Belgaum, India and a master’s degree in information systems on Data Science from University of Texas at Arlington.

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For more complete information about compiler optimizations, see our Optimization Notice.