Optimize AI Workloads: Five Use Cases to Reduce the Learning Curve

Building efficient and scalable end-to-end AI applications is complex and often comes with a steep learning curve due to the many tools, libraries, and optimization methods required.

This session introduces a solution: five turnkey, downloadable AI reference kits tailor-made to solve business problems across a variety of industries, delivering higher accuracy and better performance while decreasing development cycles. Each is built with Intel-designed AI workflows and optimized tools, frameworks, and libraries.

This video shows:

• An overview of the use cases: predictive asset maintenance, credit card fraud detection, disease prediction, correspondence indexing, and anomaly detection.
  
• How to use the kits to jumpstart development of your AI applications, including customizing them for your specific needs.
• How to run them with Docker* containers, bare metal, or Argo Workflows on Kubernetes* using the Helm* package manager.

Skill level: Novice

Many Intel®-optimized AI libraries and frameworks showcased in this session are downloadable as part of the AI Tools. They are also available as stand-alone products:

• Intel® Neural Compressor
• PyTorch* Optimizations from Intel
• TensorFlow* Optimizations from Intel
• Intel® Extension for Scikit-learn*
• Intel® Distribution of Modin*

• The AI Reference Kits Library offers overviews of and access to all 34 kits.
• Review and download a wealth of ready-to-use code samples to develop, optimize, and offload multiarchitecture applications.