arize 0.0.8

A helper library to interact with Arize AI APIs

Arize AI

Overview

A helper library to interact with Arize AI APIs

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Quickstart

Instrument your model to log predictions and latent truth events. The logged events allow the Arize platform to generate visualizations of features, model output and prediction evaluation metrics. Additionally the platform will provide data quality monitoring and data distribution alerts, for your production models.

Start logging with the following steps.

1. Create your account

Sign up for a free account by reaching out to [e-mail].

2. Get your service key

When you create an account, we generate a service api key. You will need this API Key and account id for logging authentication.

3. Instrument your code

Python Client

If you are using our python client, add a few lines to your code to log predictions and truths. Logs are sent to Arize asynchrously.

Install Library

Install our library in an environment using Python 3.

$ pip3 install arize

Or clone the repo:

$ git clone https://github.com/Arize-ai/client_python.git
$ python setup.py install

Initialize Python Client

Initialize arize at the start of your sevice using your previously created Account ID and API Key

NOTE: We suggest adding the API KEY as secrets or an environment variable.

from arize.api import Client

API_KEY = os.environ.get('ARIZE_API_KEY') #If passing api_key via env vars

arize = AsyncClient(account_id=1234, api_key=API_KEY, uri='https://dev.arize.com/v1/log')

Collect your model input features and labels you'd like to track

You can track all input features used to at prediction time by logging it via a key:value dictionary.

labels = {
    'state': 'ca',
    'city': 'berkeley',
    'lat': 37.8717,
    'lng': -122.2579,
    'pos_approved': True,
    'item_count': 10,
    'merchant_type': 'educational',
    'charge_amount': '20.11',
    }

Log Predictions

## Returns a concurrent.futures.Future
response = arize.log(
    model_id='sample-model-1',
    model_version='v1.23.64', ## Optional
    prediction_id='plED4eERDCasd9797ca34',
    prediction_value=True,
    labels=labels,
    )

## NB: This is a blocking call
res = response.result()

Arize log returns a response future object for asyncronous behavior. To capture the logging response, you can await the resolved future. If you desire a fire-and-forget pattern you can disreguard the reponse altogether.

We automatically discover new models logged over time based on the model ID sent on each prediction.

Log Truths

response = arize.log(
    model_id='sample-model-1',
    prediction_id='plED4eERDCasd9797ca34',
    truth_value=True,
    )

Once a truth for a prediction is determined, you can log those to Arize and evaluate your metrics over time. What links the truch to the original prediction is the prediction_id for a model_id

4. Log In for Analytics

That'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 feature labels, models, time, etc.

Analytics Dashboard

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Other languages

If 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 Arize

curl -X POST -H "Authorization: API_KEY" "https://dev.arize.com/v1/log" -d'{"account_id": 0, "model_id": "test_model_1", "prediction_id":"test100", "prediction":{"model_version": "v1.23.64", "labels":{"state":{"string_label": "CO"}, "item_count":{"int_label": 10}, "charge_amt":{"label_float": 12.34}, "physical_card":{"string_label": true}}, "prediction_value": {"binary_value": false}}}'

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Contributing

TBD