Package: arize
OverviewA helper library to interact with Arize AI APIs.Arize is an end-to-end ML observability and model monitoring platform. The platform is designed to help ML engineers and data science practitioners surface and fix issues with ML models in production faster with:Automated ML monitoring and model monitoringWorkflows to troubleshoot model performanceReal-time visualizations for model performance monitoring, data quality monitoring, and drift monitoringModel prediction cohort analysisPre-deployment model validationIntegrated model explainabilityQuickstartThis guide will help you instrument your code to log observability data for model monitoring and ML observability. The types of data supported include prediction labels, human readable/debuggable model features and tags, actual labels (once the ground truth is learned), and other model-related data. Logging model data allows you to generate powerful visualizations in the Arize platform to better monitor model performance, understand issues that arise, and debug your model's behavior. Additionally, Arize provides data quality monitoring, data drift detection, and performance management of your production models.Start logging your model data with the following steps:1. Create your accountSign up for a free accountHERE.2. Get your service API keyWhen you create an account, we generate a service API key. You will need this API Key and your Space Key for logging authentication.3. Instrument your codePython ClientIf you are using the Arize python client, add a few lines to your code to log predictions and actuals. Logs are sent to Arize asynchronously.Install LibraryInstall the Arize library in an environment using Python >= 3.6.$pip3installarizeOr clone the repo:$gitclonehttps://github.com/Arize-ai/client_python.git
$python3-mpipinstallclient_python/Initialize Python ClientInitialize the arize client at the start of your service using your previously created API and Space Keys.NOTE:We strongly suggest storing the API key as a secret or an environment variable.fromarize.apiimportClientfromarize.utils.typesimportModelTypes,EnvironmentsAPI_KEY=os.environ.get('ARIZE_API_KEY')#If passing api_key via env varsarize_client=Client(space_key='ARIZE_SPACE_KEY',api_key=API_KEY)Collect your model input features and labels you'd like to trackReal-time single prediction:For a single real-time prediction, you can track all input features used at prediction time by logging them via a key:value dictionary.features={'state':'ca','city':'berkeley','merchant_name':'Peets Coffee','pos_approved':True,'item_count':10,'merchant_type':'coffee shop','charge_amount':20.11,}Bulk predictions:When dealing with bulk predictions, you can pass in input features, prediction/actual labels, and prediction_ids for more than one prediction via a Pandas Dataframe where df.columns contain feature names.## e.g. labels from a CSV. Labels must be 2-D data frames where df.columns correspond to the label namefeatures_df=pd.read_csv('path/to/file.csv')prediction_labels_df=pd.DataFrame(np.random.randint(1,100,size=(features.shape[0],1)))ids_df=pd.DataFrame([str(uuid.uuid4())for_inrange(len(prediction_labels.index))])Log PredictionsSingle real-time prediction:## Returns an array of concurrent.futures.Futurepred=arize.log(model_id='sample-model-1',model_version='v1.23.64',model_type=ModelTypes.BINARY,prediction_id='plED4eERDCasd9797ca34',prediction_label=True,features=features,)#### To confirm that the log request completed successfully, await for it to resolve:## NB: This is a blocking callresponse=pred.get()res=response.result()ifres.status_code!=200:print(f'future failed with response code{res.status_code},{res.text}')Bulk upload of predictions:responses=arize.bulk_log(model_id='sample-model-1',model_version='v1.23.64',model_type=ModelTypes.BINARY,prediction_ids=ids_df,prediction_labels=prediction_labels_df,features=features_df)#### To confirm that the log request completed successfully, await for futures to resolve:## NB: This is a blocking callimportconcurrent.futuresascfforresponseincf.as_completed(responses):res=response.result()ifres.status_code!=200:print(f'future failed with response code{res.status_code},{res.text}')The client's log_prediction/actual function returns a single concurrent future while log_bulk_predictions/actuals returns a list of concurrent futures for asynchronous behavior. To capture the logging response, you can await the resolved futures. If you desire a fire-and-forget pattern, you can disregard the responses altogether.We automatically discover new models logged over time based on the model ID sent on each prediction.Logging Actual LabelsNOTE:Notice the prediction_id passed in matches the original prediction sent on the previous example above.response=arize.log(model_id='sample-model-1',model_type=ModelTypes.BINARY,prediction_id='plED4eERDCasd9797ca34',actual_label=False)Bulk upload of actuals:responses=arize.bulk_log(model_id='sample-model-1',model_type=ModelTypes.BINARY,prediction_ids=ids_df,actual_labels=actual_labels_df,)#### To confirm that the log request completed successfully, await for futures to resolve:## NB: This is a blocking callimportconcurrent.futuresascfforresponseincf.as_completed(responses):res=response.result()ifres.status_code!=200:print(f'future failed with response code{res.status_code},{res.text}')Once the actual labels (ground truth) for your predictions have been determined, you can send them to Arize and evaluate your metrics over time. The prediction id for one prediction links to its corresponding actual label so it's important to note those must be the same when matching events.Bulk upload of all your data (features, predictions, actuals, SHAP values) in a pandas.DataFrameUse arize.pandas.logger to publish a dataframe with the features, predicted label, actual, and/or SHAP to Arize for monitoring, analysis, and explainability.Initialize Arize Client fromarize.pandas.loggerfromarize.pandas.loggerimportClient,Schemafromarize.utils.typesimportModelTypes,EnvironmentsAPI_KEY=os.environ.get('ARIZE_API_KEY')#If passing api_key via env varsarize_client=Client(space_key='ARIZE_SPACE_KEY',api_key=API_KEY)Logging features & predictions only, then actualsresponse=arize_client.log(dataframe=your_sample_df,model_id="fraud-model",model_version="1.0",model_type=ModelTypes.SCORE_CATEGORICAL,environment=Environments.PRODUCTION,schema=Schema(prediction_id_column_name="prediction_id",timestamp_column_name="prediction_ts",prediction_label_column_name="prediction_label",prediction_score_column_name="prediction_score",feature_column_names=feature_cols,))response=arize_client.log(dataframe=your_sample_df,model_id=model_id,model_type=ModelTypes.SCORE_CATEGORICAL,environment=Environments.PRODUCTION,schema=Schema(prediction_id_column_name="prediction_id",actual_label_column_name="actual_label",))Logging features, predictions, actuals, and SHAP values togetherresponse=arize_client.log(dataframe=your_sample_df,model_id="fraud-model",model_version="1.0",model_type=ModelTypes.NUMERIC,environment=Environments.PRODUCTION,schema=Schema(prediction_id_column_name="prediction_id",timestamp_column_name="prediction_ts",prediction_label_column_name="prediction_label",actual_label_column_name="actual_label",feature_column_names=feature_col_name,shap_values_column_names=dict(zip(feature_col_name,shap_col_name))))4. Log In for AnalyticsThat's it! Once your service is deployed and predictions are logged you'll be able to log into your Arize account and dive into your data, slicing it by features, tags, models, time, etc.Analytics DashboardLogging SHAP valuesLog feature importance in SHAP values to the Arize platform to explain your model's predictions. By logging SHAP values you gain the ability to view the global feature importances of your predictions as well as the ability to perform cohort and prediction based analysis to compare feature importance values under varying conditions. For more information on SHAP and how to use SHAP with Arize, check out ourSHAP documentation.Other languagesIf you are using a different language, you'll be able to post an HTTP request to our Arize edge-servers to log your events.HTTP post request to Arizecurl-XPOST-H"Authorization: YOU_API_KEY""https://log.arize.com/v1/log"-d'{"space_key": "YOUR_SPACE_KEY", "model_id": "test_model_1", "prediction_id":"test100", "prediction":{"model_version": "v1.23.64", "features":{"state":{"string": "CO"}, "item_count":{"int": 10}, "charge_amt":{"float": 12.34}, "physical_card":{"string": true}}, "prediction_label": {"binary": false}}}'WebsiteVisit Us At:https://arize.com/model-monitoring/Official Documentations:https://docs.arize.com/arize/Additional ResourcesWhat is ML observability?Playbook to model monitoring in productionUsing statistical distance metrics for ML monitoring and observabilityML infrastructure tools for data preparationML infrastructure tools for model buildingML infrastructure tools for productionML infrastructure tools for model deployment and model servingML infrastructure tools for ML monitoring and observabilityVisit theArize BlogandResource Centerfor more resources on ML observability and model monitoring.