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 Data Scientists and Engineers to:

• Set up in minutes: Begin generating statistical profiles of any dataset using whylogs, the lightweight open-source library.

• Upload dataset profiles to the WhyLabs platform for centralized and customizable monitoring/alerting of dataset features as well as model inputs, outputs, and performance.

• Integrate seamlessly: interoperable with any data pipeline, ML infrastructure, or framework. Generate real-time insights into your existing data flow. See more about our integrations here.

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

• Maintain data privacy: WhyLabs relies statistical profiles created via whylogs so your actual data never leaves your environment!

Quick Start

The whylogs logging agent is the easiest way to enable logging, testing, and monitoring in a data or 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:

whylogs v0

pip install "whylogs<1.0"

whylogs v1

pip install whylogs

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

whylogs v0

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")

whylogs v1

import whylogs as why

import pandas as pd

df = pd.read_csv("path/to/file.csv")

# dataframe

results = why.log(pandas=df)

# dict

results = why.log({'column_a':1.0, 'column_b':2.0})

# image

# coming soon!

# extract the profile for viewing, tracking, or saving to disk

profile = results.profile()

Onboarding to WhyLabs

With whylogs integrated into your workflow, the next step is to onboard with the WhyLabs SaaS Platform to monitor dataset features, model inputs, outputs, and performance. Get started yourself for free or contact us for a demo!

For more details on how to quickly get value out of whylogs/WhyLabs, visit our onboarding page.

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 takes minutes.
• whylogs seamlessly integrates 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

• whylogs: Embrace Data Logging Across Your ML Systems