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Overview

This tutorial runs through how to use Arize in a Continuous Integration and Continuous Deployment workflow for models. This tutorial is based on Continuous Machine Learning Groups work:
https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/cml.jpg

CML · Continuous Machine Learning

CML · Continuous Machine Learning”
 This tutorial will show you how to:
  • Integrate Arize into the CI/CD workflow
  • Run Arize on Validation data every time a new model version is checked in
  • Capture data in the platform to compare different validated models

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Workflow Overview

The CI/CD workflow for models with CML involves a training script and a linkage to Github actions.

CI CD CML Example

The following describes the steps for training and validation runs:
  1. A model directory is setup on Github which contains both model file and train scripts for CML
  2. Train scripts are built to run a set of inferences across any newly built model
  3. GitHub actions are setup to run the train script on any model checkin
  4. On Model Checkin the train script is run
    1. The train Script logs the validation inferences to Arize
    2. Checks within the Arize platform can be setup to run on every new validation batch of data. These checks can include comparing against previous model data or fixed levels analysis
    3. On check failure dashboards can be created for model analysis
    4. Future*: The ability to quickly poll through API the validation checks as part of Github actions for pass / fail*
An example Train script for CML with Arize is included here: The github/workflows directory defines the github actions that are run on model checkin. This is derived from the CML example.

tutorials_python/train.py at main · Arize-ai/tutorials_python

GitHub
 The Train scripts typically have 2 parts:
  1. Train and Score the latest model
#################################
########## MODELLING ############
#################################

# Fit a model on the train section
regr = RandomForestRegressor(max_depth=2, random_state=seed)
regr.fit(X_train, y_train)

# Report training set score
train_score = regr.score(X_train, y_train) * 100
# Report test set score
test_score = regr.score(X_test, y_test) * 100
y_pred = regr.predict(X_test)
2. Log artifacts and data to CML 
# Write scores to a file
with open("metrics.txt", 'w') as outfile:
        outfile.write("Training variance explained: %2.1f%%\n" % train_score)
        outfile.write("Test variance explained: %2.1f%%\n" % test_score)
An additional 3rd section is added to send the feature data, inferences (predictions) and ground truth (actuals) to Arize 
#############################################
########## Arize AI Validation Sample ############
#############################################

SPACE_ID="SPACE_ID"
API_KEY="API_KEY"
model_name = "validation-wine-model-cicd"


datetime_rightnow = datetime.datetime.today()
model_version_id_now = 'train_validate_' + datetime_rightnow.strftime('%m_%d_%Y__%H_%M_%S')
id_df = pd.DataFrame([str(id) + model_version_id_now for id in X_test.index])
arize_client = Client(space_id=SPACE_ID, api_key=API_KEY,uri='https://devr.arize.com/v1')
tfuture = arize_client.log(model_id=model_name, model_version=model_version_id_now,
                           features=X_test, prediction_ids=id_df,
                           prediction_labels=pd.DataFrame(y_pred))
tfuture = arize_client.log(model_id=model_name, model_version=model_version_id_now,
                           prediction_ids=id_df, actual_labels=pd.DataFrame(y_test))
If using version < 4.0.0, replace space_id=SPACE_ID with organization_key=SPACE_ID
The above workflow can be modified for any model CI CID flow where scoring is done from a set of validation inferences.