Note: If you are new to PyTorch, MLflow, or RedisAI, I will introduce them briefly where needed, with a few references. However, the main aim of this blog post is not to run through these tools, but rather to provide an example that shows how these tools can be used together to build a spectacular workflow for your deep learning infrastructure.
Another note: At the time of writing this blog, MLflow is at 1.10.0 and does not yet have the
torchscript flavor integrated. The example here requires this pull request to be merged in order to enable
torchscript support. …
Well, kinda :-)
TLDR; Do you need to deploy a deep learning model to production? Do you know how to operate Redis? There might be something in for you.
2017–2019: We have seen the rise of a few model servers in the last three years from different tech giants and other organizations. A few promising entities are Google’s Tensorflow serving, Amazon’s MxNet Model Server, Nvidia’s TensorRT and Berkeley’s Clipper. While all of these represent great options for deploying deep learning to production, the production landscape is still rife with opportunities for addressing the challenges of reliability and scale. Well, it’s…
Some times I wonder, why machines are still dumb. Then i realize, its the human who make the machine