Watch the recording now and learn: 

• The 3 key computational workloads of Artificial Intelligence and Machine Learning (AI/ML)
  1. Training, inference, and analytics
  2. Challenges in optimizing workloads across different resources
• The various AL/ML data types and how they are accelerated through Tensor Core processing
  1. Tradeoffs of FP32 vs FP16 in traditional ML training
  2. BFloat32 as a compromise to F16 limitations
  3. New TF32 data type, performance gains, and how to use it
• How to access pre-trained models, containers, and helm charts via NVIDIA’s NGC to speed development time and time to solution