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ML engineering pipelines

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

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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…

13 0 Open

ML engineering pipelines easy

How to Run Batch Predictions with a Mock Model in Python

Build a lightweight mock model class and run predictions across a batch of samples, returning results as a plain Python list.

numpy batch ml

Python

import numpy as np

class MockModel:
    def __init__(self, weights):
        self.weights = np.array(weights)

    def predict(self, X):
        return X @ self.weights

def predict_batch(model, batch):
    """Run predictions for a batch of samples and return results as a list."""
    return model.predict(np.array(ba…

14 0 Open

ML engineering pipelines easy

How to Save and Load a Mock Model with Pickle and joblib in Python

Serialize a custom machine learning model to a .joblib file with joblib.dump, reload it, and run a prediction with joblib.load.

joblib pickle model-serialization

Python

import joblib
from pathlib import Path

class MockModel:
    def __init__(self, weights):
        self.weights = weights

    def predict(self, features):
        return sum(w * f for w, f in zip(self.weights, features))


def save_model_pickle(model, filepath):
    with open(filepath, "wb") as f:
        joblib.dump(…

16 0 Open

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