Model Outlier Detection¶
This demo is based on VAE outlier detection in the alibi detect project. Here we will :
Launch an image classifier model trianed on the CIFAR10 dataset
Setup an outlier detector for this particular model
Send a request to get a image classification
Send a perturbed request to get a outlier detection
This demo requires Knative installation on the cluster as the outlier detector will be installed as a kservice.
Use the following model uri with
tensorflow runtime. Set the protocol to ‘tensorflow’:
Setup Outlier detector¶
Setup an outlier detector with model name
cifar10 using the default settings (which sets Reply URL as seldon-request-logger in the logger’s default namespace - change if you modified this at install time) and storage URI as follows:
Run a single prediction using the tensorflow payload format of an image
truck. Also a perturbed image of the truck in the same format at
outlier truck image. Make a couple of these requests at random using the predict tool in the UI.
View outliers on the Requests Screen¶
Go to the requests screen to view all the historical requests. You can see the outlier value on each instance. Also you can highlight outliers based on this score and also use the filter to see only the outliers as needed.
Monitor outliers on the Monitor Screen¶
Under the ‘Monitor’ section you can see a timeline of outlier requests.