Fraud detection workflow example#676
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Signed-off-by: Jaya Venkatesh <jjayabaskar@nvidia.com>
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Signed-off-by: Jaya Venkatesh <jjayabaskar@nvidia.com>
Signed-off-by: Jaya Venkatesh <jjayabaskar@nvidia.com>
Signed-off-by: Jaya Venkatesh <jjayabaskar@nvidia.com>
Signed-off-by: Jaya Venkatesh <jjayabaskar@nvidia.com>
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I've only taken a quick look, but I noticed that the instructions are quite generic, and I think it would be helpful if we choose a provider (AWS, Brev or whatever is easier) and have the end to end example shown. Currently, when I read it I'm no sure the user will be quite sure where to start or how to set things up. How did you set up things and do the running? |
Signed-off-by: Jaya Venkatesh <jjayabaskar@nvidia.com>
Signed-off-by: Jaya Venkatesh <jjayabaskar@nvidia.com>
…esh19/deployment into fraud-detection-mlops-example
Signed-off-by: Jaya Venkatesh <jjayabaskar@nvidia.com>
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Towards #667
Adds a new workflow example demonstrating how to wrap the NVIDIA Financial Fraud Detection AI
Blueprint in production infrastructure
using Prefect (orchestration), MLflow (experiment tracking + model registry), and Triton (champion/challenger
serving with native versioning).
The notebook walks through the architecture, each pipeline stage, running and monitoring the pipeline, and
scaling to multiple machines.