Learn how to get started using Arize!
Arize integrates with your ML stack, no matter where your data is hosted
!pip install arize
from arize.pandas.logger import Client, Schema
from arize.utils.types import ModelTypes, Environments, Schema, Metrics
API_KEY = 'YOUR API KEY'
SPACE_KEY = 'YOUR SPACE KEY'
arize_client = Client(space_key=SPACE_KEY, api_key=API_KEY)
For this example, download the
load_breast_cancerdataset, assign the dataset to a variable, and preview the data to better understand what we're working with.
from sklearn.datasets import load_breast_cancer
breast_cancer_dataset = load_breast_cancer()
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:
breast_cancer_features = breast_cancer_dataset['data'] # feature data
breast_cancer_feature_names = breast_cancer_dataset['feature_names'] # feature names
breast_cancer_targets = breast_cancer_dataset['target'] # actual data
breast_cancer_target_names = breast_cancer_dataset['target_names'] # actual labels
breast_cancer_taget_namesto their corresponding
breast_cancer_targetsto use as a human-comprehensible list of actual labels.
target_name_transcription =  # this will become our list of actuals
for i in breast_cancer_targets:
Create a Pandas dataframe to use the Arize Python Pandas logger with our predefined features and actuals(
Note: We've duplicated the
actual_labelcolumn to create a
prediction_labelcolumn for simplicities sake. Data will not populate in the Arize platform without a record of prediction data.
import pandas as pd
df = pd.DataFrame(breast_cancer_features, columns=breast_cancer_feature_names)
df['actual_label'] = target_name_transcription
df['prediction_label'] = target_name_transcription
# this is optional, but makes this example more interesting in the platform
df['prediction_label'] = df['prediction_label'].iloc[::-1].reset_index(drop=True)
schema = Schema(
'mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness', 'mean compactness', 'mean concavity',
'mean concave points', 'mean symmetry', 'mean fractal dimension',
'radius error', 'texture error', 'perimeter error', 'area error',
'smoothness error', 'compactness error', 'concavity error',
'concave points error', 'symmetry error',
'fractal dimension error', 'worst radius', 'worst texture',
'worst perimeter', 'worst area', 'worst smoothness',
'worst compactness', 'worst concavity', 'worst concave points',
'worst symmetry', 'worst fractal dimension'
response = arize_client.log(
It usually takes ~10 minutes for Arize to populate data throughout the platform. We recommend grabbing a quick cup of coffee (or tea) in the meantime!
Now that you've uploaded some data to Arize, check it out on the platform. Navigate to the 'Performance Tracing' tab within your model. Here, you'll see an interactive performance-over-time chart and a performance breakdown visualization.
Performance Breakdown & Performance Insights
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.
Prebuilt monitors in the Monitor's Setup tab
Use our various alerting integrations or alert via email
We get it - ML observability is a lot of fun! Keep an eye on key model health metrics with dashboards 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!
Example dashbaord with key performance metrics
Connect your Cloud Storage Blob or Data Warehouse to automatically sync model data with Arize!