Intel® Optimization for TensorFlow*: Tips & Tricks for an AI & High-Performance Computing (HPC) Convergence

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

Preethi Venkatesh, an AI technical consulting engineer, discusses how developers can achieve different levels of optimizations and see performance benefits using this Intel-optimized tool. The session includes use-case demonstrations, case studies, and benchmarks. She also answers questions from the audience.

• Download the Intel Optimization for TensorFlow as part of the AI Tools (which also includes PyTorch* and Intel® Distribution for Python*)
• Download the stand-alone tool

Preethi Venkatesh

Technical consulting engineer, Intel Corporation

Preethi helps 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 four-year tour at Infosys Limited* where she was a business data analyst.

Preethi has a BA degree in instrumentation technology from Visvesvaraya Technological University in Belgaum, India, and an MA degree in information systems on data science from the University of Texas at Arlington.