NVIDIA TAO: A Robust ML Platform with Transfer Learning Capabilities

 NVIDIA tools for BYOM and Transfer Learning

The NVIDIA TAO toolkit is quite impressive. The capabilities around the #BYOM principle are pretty robust. 

The TAO architecture stands on the Docker container engine, which supports running applications similar to the Docker Compose workflow. In addition, the capability for running the pipeline in an isolated filesystem and configuration environment obviously exists. The NVIDIA nGPU works for #DOCKER and provides support for video card computation with #CUDA.  

The transfer learning process is very user-friendly. In the transfer learning process within #TAO, we can reference pre-trained neural network layers and place it in another project to initialize the weights of the target model. As a result, the target model can be trained faster or use a different data set for training data. Of course, the results need to be carefully monitored, but fundamentally, this approach worked for me several times when I needed "enrichment" in a target model. #BYOM #transferlearning #AI #machinelearning #architecture #dataanalytics

https://www.youtube.com/watch?v=_aLtQXkxcJ8