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Copyright © 2023 Arize AI, Inc
Last updated
Install arize
, import dependencies, and attach your
To easily get started, we'll prepare a simple Classification Model dataset from to send via the . For this example, download the load_breast_cancer
dataset, assign the dataset to a variable, and preview the data to better understand what we're working with.
The dataset contains all the information we need to create a Pandas dataframe. For any dataset, extract the features, predictions, and actuals data. For this example:
Assign breast_cancer_taget_names
to their corresponding breast_cancer_targets
to use as a human-comprehensible list of actual labels.
Create a Pandas dataframe to use the Arize Python Pandas logger with our predefined features and actuals(target_name_transcription
).
Note: We've duplicated the actual_label
column to create a prediction_label
column for simplicities sake. Data will not populate in the Arize platform without a record of prediction data.
Create monitors to keep an eye on key performance, drift, and data quality metrics. Navigate to the 'Monitors' tab and enable relevant prebuilt monitors for your use case.
Connect your Cloud Storage Blob or Data Warehouse to automatically sync model data with Arize!
Define the so Arize knows what your columns correspond to. the model data.
Now that you've uploaded some data to Arize, check it out on the platform. Navigate to the '' tab within your model. Here, you'll see an interactive performance-over-time chart and a performance breakdown visualization.
Configure on the 'Config' page within the monitor's tab to keep you posted when your model changes unexpectedly.
We get it - ML observability is a lot of fun! Keep an eye on key model health metrics with for a single pane of glass view of your model. Create a custom dashboard, use a pre-built template, and simply copy and paste the dashboard URL to share with your team!
Learn how to get started using Arize!