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Deploying and visualizing models
isaacmg edited this page Jun 14, 2020
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Goals
- Easily re-train and re-deploy models
- Analyze models performance both over historical test set and new test data.
Deploying models
- Dockerize model (this should be handled automatically in flow). Dockerfile should contain necessary packages.
- Model weights are automatically uploaded to GCS into a bucket called
ts-model-prod
- Dockerfile should take as input path to the weight file. So that all that requires changing to updated model.
(Re)-Deployment Architecture
- New data gathers over a 50 day period.
- This data is split into two partitions. 18 of these days will be added to the test set and the remaining 32 days will be incorporated as new training data.
- Model preforms better on the previous historical dataset
- Both old model and new model run on the newly acquired test set.