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Introduction

AI Observability with whylogs and WhyLabs#

WhyLabs is an observability platform designed to monitor data pipelines and ML applications for data quality regressions, data drift and model performance degradation. Built on top of an open-source package called whylogs, the platform enables AI builders to:

  • Set up in minutes: provision the platform using whylogs, the lightweight open-source library.

  • Integrate seamlessly: interoperable with any ML infrastructure and framework. Generate real-time insights in your existing data flow.

  • Scale to terabytes: handle your large-scale data, keeping compute requirements low. Integrate with either batch or streaming data pipelines.

Quick start#

The whylogs logging agent is the easiest way to enable logging, testing, and monitoring in an ML/AI application. The lightweight agent profiles data in real-time, collecting thousands of metrics from structured data, unstructured data, and ML model predictions with zero configuration.

First, install whylogs:

pip install whylogs

Then, start logging statistical properties of features, model inputs, and model outsputs to enable explorative analysis, data unit testing, and monitoring. Getting whylogs up-and-running is easy:

from whylogs import get_or_create_session
import pandas as pd
session = get_or_create_session()
df = pd.read_csv("path/to/file.csv")
with session.logger(dataset_name="my_dataset") as logger:
#dataframe
logger.log_dataframe(df)
#dict
logger.log({"name": 1})
#images
logger.log_images("path/to/image.png")

Onboarding to WhyLabs#

With whylogs integrated into your workflow, the next step is to onboard with the WhyLabs SaaS Platform to monitor model inputs, outputs, and performance. Onboarding only takes a few minutes, so please contact us to request an account.

Learn more about whylogs - open source logging agent#

  • whylogs provides lightweight data collection, enterprise scalability, and flexibility designed for data science
  • workflows. It has built-in data tagging and aggregation capabilities. Furthermore, the installation to take minutes and
  • seamlessly integrate with existing tools. You can read an in-depth overview about whylogs.

Wondering if the whylogs is a good fit for your use case? Check out our use cases section or join our Slack channel.

Contribute to whylogs?#

Check out the whylogs contribution process and Code of Conduct to get started.

Choose between the whylogs Python GitHub repo or the whylogs Java GitHub repo for your contributions.

Brainstorm ideas and share feedback with the whylogs community members on Slack!

Resources#