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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 Generate Experiment Tracking Run IDs in Python

Generate unique experiment run IDs with timestamps and random suffixes for tracking ML pipeline executions.

run-ids experiment-tracking ml-pipelines

import random
import string
import time

def generate_run_id(prefix="exp"):
    timestamp = time.strftime("%Y%m%d_%H%M%S")
    suffix = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
    return f"{prefix}_{timestamp}_{suffix}"

if __name__ == "__main__":
    # Simulate tracking three experiment r…

13 0 Open

ML engineering pipelines medium

How to Mock MLflow Model Registration in Python

Build a lightweight in-memory mock of MLflow's MlflowClient to test model registration, versioning, and stage transitions without a tracking server.

mlflow mocking model-registry

Python

from mlflow.tracking import MlflowClient
from mlflow.entities import ModelVersion, Model


class MockMlflowClient:
    """Minimal mock of MlflowClient's model registration methods."""
    
    def __init__(self):
        self.registered_models = {}
        self.model_versions = {}
    
    def register_model(self, mod…

14 0 Open

ML engineering pipelines easy

How to Mock MLflow log_params and log_metrics in Python

Use unittest.mock to patch MLflow's log_param and log_metric, run the training function, and verify logging calls without touching a real tracking server.

mlflow mock testing

Python

from unittest.mock import Mock, patch
import mlflow


def train_model():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_param("epochs", 10)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.log_metric("loss", 0.05)
    return "Training completed"


if __name__ == "__main__":
    with patch("mlflow.log_par…

15 0 Open

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