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Version: 0.16.16

Integrating ZenML With Great Expectations

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ZenML helps data scientists and ML engineers to make Great Expectations data profiling and validation an integral part of their production ML toolset and workflows. ZenML is an extensible open source MLOps framework for creating portable, production-ready ML pipelines.

ZenML eliminates the complexity associated with setting up the Great Expectations Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. for use in production by integrating it directly into its MLOps tool stack construct. This allows you to start using Great Expectations in your ML pipelines right away, with all the other great features that ZenML brings along: portability, caching, tracking and versioning and immediate access to a rich ecosystem of tools and services that spans everything else MLOps.

Prerequisites​

  • An understanding of Great Expectations Expectation SuitesA collection of verifiable assertions about data., Validation ResultsGenerated when data is Validated against an Expectation or Expectation Suite., and Data DocsHuman readable documentation generated from Great Expectations metadata detailing Expectations, Validation Results, etc. concepts
  • Some understanding of ZenML pipelines and steps.

ZenML ships with a couple of builtin pipeline steps that take care of everything from configuring temporary DatasourcesProvides a standard API for accessing and interacting with data from a wide variety of source systems., Data Connectors, and Runtime Batch RequestsProvided to a Datasource in order to create a Batch. to access in-memory datasets to setting up and running ProfilersGenerates Metrics and candidate Expectations from data., ValidatorsUsed to run an Expectation Suite against data. and CheckpointsThe primary means for validating data in a production deployment of Great Expectations., to generating the Data DocsHuman readable documentation generated from Great Expectations metadata detailing Expectations, Validation Results, etc. for you. These details are abstracted away from you and all you have left to do is simply insert these steps into your ML pipelines to run either data profiling or data validation with Great Expectations on any input Pandas DataFrame.

Also included is a ZenML visualizer that gives you quick access to the Data Docs to display Expectation Suites and Validation Results generated, versioned and stored by your pipeline runs.

Dev loops unlocked by integration​

  • Implement the Great Expectations "golden path" workflow: teams can create Expectation Suites and store them in the shared ZenML Artifact Store, then use them in their ZenML pipelines to automate data validation. All this with Data Docs providing a complete report of the overall data quality status of your project.
  • Start with Great Expectations hosted exclusively on your local machine and then incrementally migrate to production ready ZenML MLOps stacks as your project matures. With no code changes or vendor lock-in.

Setup​

This simple setup installs both Great Expectations and ZenML and brings them together into a single MLOps local stack. More elaborate configuration options are of course possible and thoroughly documented in the ZenML documentation.

1. Install ZenML​

pip install zenml

2. Install the Great Expectations ZenML integration​

zenml integration install -y great_expectations

3. Add Great Expectations as a Data Validator to the default ZenML stack​

zenml data-validator register ge_data_validator --flavor great_expectations
zenml stack update -dv ge_data_validator
tip

This stack uses the default local ZenML Artifact Store that persists the Great Expectations Data Context information on your local machine. However, you can use any of the Artifact Store flavors shipped with ZenML, like AWS, GCS or Azure. They will all work seamlessly with Great Expectations.

Usage​

Developing ZenML pipelines that harness the power of Great Expectations to perform data profiling and validation is just a matter of instantiating the builtin ZenML steps and linking them to other steps that ingest data. The following examples showcase two simple pipeline scenarios that do exactly that.

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To run the examples, you will also need to install the ZenML scikit-learn integration:

zenml integration install -y sklearn

Great Expectations Zenml data profiling example​

This is a simple example of a ZenML pipeline that loads data from a source and then uses the ZenML builtin Great Expectations profiling step to infer an Expectation Suite from that data. After the pipeline run is complete, the Expectation Suite can be visualized in the Data Docs.

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The following Python code is fully functional. You can simply copy it in a file and run it as-is, assuming you installed and setup ZenML properly.

import pandas as pd
from sklearn import datasets

from zenml.integrations.constants import GREAT_EXPECTATIONS, SKLEARN
from zenml.integrations.great_expectations.steps import (
    GreatExpectationsProfilerConfig,
    great_expectations_profiler_step,
)
from zenml.integrations.great_expectations.visualizers import (
    GreatExpectationsVisualizer,
)
from zenml.pipelines import pipeline
from zenml.steps import Output, step


#### 1. Define ZenML steps


@step(enable_cache=False)
def importer(
) -> Output(dataset=pd.DataFrame, condition=bool):
    """Load and return a random sample of the University of Wisconsin breast
    cancer diagnosis dataset.
    """
    breast_cancer = datasets.load_breast_cancer()
    df = pd.DataFrame(
        data=breast_cancer.data, columns=breast_cancer.feature_names
    )
    df["class"] = breast_cancer.target
    return df.sample(frac = 0.5), True

# instantiate a builtin Great Expectations data profiling step
ge_profiler_step = great_expectations_profiler_step(
    step_name="ge_profiler_step",
    config=GreatExpectationsProfilerConfig(
        expectation_suite_name="breast_cancer_suite",
        data_asset_name="breast_cancer_df",
    )
)


#### 2. Define the ZenML pipeline


@pipeline(required_integrations=[SKLEARN, GREAT_EXPECTATIONS])
def profiling_pipeline(
    importer, profiler
):
    """Data profiling pipeline for Great Expectations."""
    dataset, _ = importer()
    profiler(dataset)


#### 4. Instantiate and run the pipeline


profiling_pipeline(
    importer=importer(),
    profiler=ge_profiler_step,
).run()


#### 5. Visualize the Expectation Suite generated, tracked and stored by the pipeline


last_run = profiling_pipeline.get_runs()[-1]
step = last_run.get_step(name="profiler")
GreatExpectationsVisualizer().visualize(step)

Great Expectations Zenml data validation example​

This is a simple example of a ZenML pipeline that loads data from a source and then uses the ZenML builtin Great Expectations data validation step to validate that data against an existing Expectation Suite and generate Validation Results. After the pipeline run is complete, the Validation Results can be visualized in the Data Docs.

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This example assumes that you already have an Expectations Suite named breast_cancer_suite that has been previously stored in the Great Expectations Data Context. You should run the Great Expectations Zenml data profiling example first to ensure that, or create one by other means.

tip

The following Python code is fully functional. You can simply copy it in a file and run it as-is, assuming you installed and setup ZenML properly.

import pandas as pd
from great_expectations.checkpoint.types.checkpoint_result import (
    CheckpointResult,
)
from sklearn import datasets

from zenml.integrations.constants import GREAT_EXPECTATIONS, SKLEARN
from zenml.integrations.great_expectations.steps import (
    GreatExpectationsValidatorConfig,
    great_expectations_validator_step,
)
from zenml.integrations.great_expectations.visualizers import (
    GreatExpectationsVisualizer,
)
from zenml.pipelines import pipeline
from zenml.steps import Output, step


#### 1. Define ZenML steps


@step(enable_cache=False)
def importer(
) -> Output(dataset=pd.DataFrame, condition=bool):
    """Load and return a random sample of the University of Wisconsin breast
    cancer diagnosis dataset.
    """
    breast_cancer = datasets.load_breast_cancer()
    df = pd.DataFrame(
        data=breast_cancer.data, columns=breast_cancer.feature_names
    )
    df["class"] = breast_cancer.target
    return df.sample(frac = 0.5), True

# instantiate a builtin Great Expectations data profiling step
ge_validator_step = great_expectations_validator_step(
    step_name="ge_validator_step",
    config=GreatExpectationsValidatorConfig(
        expectation_suite_name="breast_cancer_suite",
        data_asset_name="breast_cancer_test_df",
    )
)

@step
def analyze_result(
    result: CheckpointResult,
) -> bool:
    """Analyze the Great Expectations validation result and print a message
    indicating whether it passed or failed."""
    if result.success:
        print("Great Expectations data validation was successful!")
    else:
        print("Great Expectations data validation failed!")
    return result.success


#### 2. Define the ZenML pipeline


@pipeline(required_integrations=[SKLEARN, GREAT_EXPECTATIONS])
def validation_pipeline(
    importer, validator, checker
):
    """Data validation pipeline for Great Expectations."""
    dataset, condition = importer()
    results = validator(dataset, condition)
    checker(results)


#### 4. Instantiate and run the pipeline


validation_pipeline(
    importer=importer(),
    validator=ge_validator_step,
    checker=analyze_result(),
).run()


#### 5. Visualize the Validation Results generated, tracked and stored by the pipeline


last_run = validation_pipeline.get_runs()[-1]
step = last_run.get_step(name="validator")
GreatExpectationsVisualizer().visualize(step)

Further discussion​

Things to consider​

The Great Expectations builtin ZenML steps and visualizer are a quick and convenient way of bridging the data validation and ML pipelines domains, but this convenience comes at a cost: there is little flexibility in the way of dataset types and configurations for Great Expectations Checkpoints, Profiles and Validators.

If the builtin ZenML steps are insufficient, you can always implement your own custom ZenML pipeline steps that use Great Expectations while still benefiting from the other ZenML integration features:

  • the convenience of using a Great Expectations Data Context that is automatically configured to connect to the infrastructure of your choise
  • the ability to version, track and visualize Expectation Suites and Validation Results as pipeline artifacts
  • the freedom that comes from being able to combine Great Expectations with a wide range of libraries and services in the ZenML MLOps ecosystem providing functions like ML pipeline orchestration, experiment and metadata tracking, model deployment, data annotation and a lot more

When things don't work​

  • Refer to the ZenML documentation for in-depth instructions on how to configure and use Great Expectations with ZenML.
  • Reach out to the ZenML community on Slack and ask for help.

Other resources​