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 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 …
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