Reference library

ML engineering pipelines

Feature prep, batch inference, model-serving hooks, and production ML workflow glue.

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ML engineering pipelines easy

How to Define Dagster ML Assets in Python

Define a chain of Dagster software-defined assets that compute raw features, normalized features, and predictions for an ML pipeline.

dagster ml-pipeline asset
Python 
from dagster import asset


@asset
def raw_features():
    return {"sepal_length": [5.1, 4.9, 6.2], "sepal_width": [3.5, 3.0, 3.4]}


@asset
def normalized_features(raw_features):
    values = raw_features["sepal_length"]
    mean = sum(values) / len(values)
    std = (sum((x - mean) ** 2 for x in values) / len(values…
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