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Chalk is the fastest way to deploy new features and feature pipelines to production. This demo covers creating a new feature. You’ll develop, unit test, integration test, and deploy a resolver for this feature. Then you’ll see how to backfill that feature to Chalk’s offline store so that data scientists can use it in historical training sets.


The first step is to write a new feature and resolver for that feature. Imagine you wanted to create a new feature called user.name_email_match_score. This feature should capture the similarity between a user’s name and email. Put another way: andy and should produce a high score, and emily and should produce a low score.

The first step is to add the new feature, and a resolver for that feature.
class User:
  fullname: str
  email: str
  name_email_match_score: float

def name_email_match_scorer(
    fullname: User.fullname
) -> User.name_email_match_score:
    email = email.split("@")[0]
    intersection = set(fullname) & set(email)
    union = set(fullname) | set(email)
    jaccard_similarity = len(intersection) / len(union)
    return jaccard_similarity

Our new resolver name_email_match_scorer takes a dependency on the user’s email and fullname in the argument list. Then, it declares that it returns the User.name_email_match_score in the return type signature.

In the body of the function, we compute the Jaccard index between the email without the domain and the full name.


Now that you’ve written the new feature and resolver, it’s time to validate your change. Chalk supports both unit testing and integration testing of your feature pipelines.

Unit test

You can unit test resolvers just like normal Python functions using any unit testing framework:

from pytest import approx

def test_name_email_match_scorer():
    assert approx(0.60) == name_email_match_scorer(
        "Katherine Johnson",
    assert approx(0.39) == name_email_match_scorer(
        "Eleanor Roosevelt",

Here, the Jaccard index for is higher with the name Katherine Johnson than with the name Eleanor Roosevelt, as expected.

Deploy branch

Now that the unit tests have passed, you can create a Branch Deploy with the new changes.

Chalk allows you to create an unlimited number of branch deployments. Branch deployments run all of your resolvers in the same way that they run in production. However, branch deployments don’t impact the offline store.

chalk apply --no-promote

Take note of the deployment id from this step — you’ll use it later.

Integration test

With the Branch Deploy in hand, you can integration test your changes.
def test_get_features():
    client = ChalkClient()
    resp = client.query(
        input={ 1},
    assert resp.get_feature_value(User.credit_report.fico_score) == 700
    assert resp.get_feature_value( == ""

Here, the Chalk API client is configured to use the preview deployment id returned in the previous step.


Once you’ve tested your changes, it’s time to deploy! This step looks much like the preview deployment, but this time without the flag --no-promote:

chalk apply

Now, your production environments can request the new user.name_email_match_score feature.

chalk query --in --out user.name_email_match_score


The user.name_email_match_score feature is live! But historically, this feature did not exist, and you won’t be able to sample its values at previous times until you run a migration.

A migration re-runs a resolver by sampling all possible previous calls to the resolver. You can also choose exactly which values to re-run with additional arguments to the chalk migrate command.

Planning the migration

Before you run the migration, you can optionally determine the expected impact of our changes with the --plan flag:

chalk migrate --resolver example.name_email_match_scorer --plan

Running the migration

Once you’re happy with the migration plan, you can execute the migration with chalk migrate:

chalk migrate --resolver example.name_email_match_scorer --parallelism 4

Offline training

With the feature backfilled, you can query the historical value:

Chalk AI - Documentation Reference
Jupyter Notebook
Chalk AI - Alerts

That’s how you deploy a new feature with Chalk! Let a member of our technical team know if we can be helpful.


Mistakes are inevitable, but Chalk provides the tools you need to quickly and easily recover and keep going.

Drop a feature

If you need to change a feature’s type (for example from string to int), or if you want to drop all the data for a feature value, the Chalk CLI has you covered with chalk drop. Simply execute the command from the CLI and you’ll have a fresh start to recreate the feature and its data as necessary.

Delete a row

Sometimes you just need to fully remove a record from your systems, whether because of a GDPR mandated “right to forget” request or due to a business requirement. Chalk provides the chalk delete command to meet this need.