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ML engineering pipelines

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

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Create a Minimal Great Expectations Suite Mock in Python

Build a small Python class that mimics a Great Expectations suite, storing and serializing column expectations as JSON.

import json


class GreatExpectationsSuite:
    """A minimal mock of a Great Expectations suite."""

    def __init__(self, suite_name, expectations=None):
        self.suite_name = suite_name
        self.expectations = expectations or []

    def add_expectation(self, expectation_type, column=None, kwargs=None):
   …

11 0 Open

ML engineering pipelines easy

How to Build a Data Validation Schema in Python

Create a lightweight validation schema using dataclasses and lambda validators to check fields in a dictionary.

validation dataclasses ml-pipelines

Python

import re
from dataclasses import dataclass, field
from typing import Any, Callable


@dataclass
class Field:
    name: str
    validator: Callable[[Any], bool]
    required: bool = True

    def validate(self, value: Any) -> bool:
        if not self.required and value is None:
            return True
        return …

12 0 Open

ML engineering pipelines medium

K-Fold Cross Validation in Python: A Simple Implementation

Implements k-fold cross validation from scratch, splitting data into folds and computing MSE scores for a baseline mean-predictor model.

cross-validation ml model-evaluation

Python

import random
from statistics import mean


def cross_validation_scores(data, labels, k=5, seed=42):
    random.seed(seed)
    indices = list(range(len(data)))
    random.shuffle(indices)
    fold_size = len(indices) // k
    folds = []
    for i in range(k):
        if i == k - 1:
            folds.append(indices[i *…

16 0 Open

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