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ML engineering pipelines

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

3 matches

Build a Data Helper Class in Python for ML Pipelines

A beginner-friendly Python class that summarizes, filters, and exports ML dataset rows as JSON.

from typing import List, Dict, Any
import json

class DataHelper:
    """Beginner-friendly helpers for ML data pipelines."""
    
    def __init__(self, data: List[Dict[str, Any]]):
        self.data = data
        self.keys = list(data[0].keys()) if data else []
    
    def summary(self) -> Dict[str, Any]:
        "…

15 0 Open

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…

13 0 Open

ML engineering pipelines medium

How to Train a Gradient Boosting Regressor in Python

Build and evaluate a scikit-learn GradientBoostingRegressor on a synthetic dataset, printing test MSE and feature importances.

sklearn gradient-boosting regression

Python

import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error

def train_gradient_boosting_mock():
    # Toy regression dataset
    np.random.seed(42)
    X = np.random.rand(100, 3) * 10
    y = 2 * X[:, 0] - 1.5 * X[:, 1] + 0.5 * X[:, 2] + np.random.normal(0,…

13 0 Open

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