Rev Up End-to-End AI Pipelines with Intel-Optimized Python Libraries

Roy Allela, Intel
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Get near-native speed from your AI and data science workloads—even in accelerated computing environments—with minimal code changes.

Calling all Python developers focused on AI/ML. Achieve highly efficient multithreading, vectorization, and memory management, and scale scientific computations across a cluster using the optimized packages found in Intel® Distribution for Python.

Attend this session to advance your Python coding skills, including developing for accelerated compute with Data Parallel Python technologies.

You will be able to:

  • Achieve drop-in acceleration—end-to-end—for your AI and data science pipelines
  • Seamlessly scale Pandas workflows across multi-node dataframes
  • Increase machine learning model accuracy and performance with Intel-optimized scikit-learn algorithms and XGBoost

Sign up today.