ML engineering pipelines
Feature prep, batch inference, model-serving hooks, and production ML workflow glue.
Bayesian Optimization in Python: A Simplified Mock Implementation
A toy Bayesian optimization loop with a Gaussian process prior, expected improvement acquisition, and noisy sampling to find a function's minimum.
import random
import math
class BayesianOptimizer:
def __init__(self, noise=0.1):
self.noise = noise
self.observations = []
def objective(self, x):
return (math.sin(3*x) + 0.5*x) / (1 + x**2)
def gaussian_process_prior(self, x1, x2, length_scale=0.5):
return math.…How to Do Random Search for Hyperparameter Tuning in Python
A mock random search that samples hyperparameter combinations from a grid and ranks them by a dummy score, with a reproducible seed.
import random
# Mock random search over a small hyperparameter grid
param_grid = {
"learning_rate": [0.001, 0.01, 0.1],
"batch_size": [16, 32, 64],
"num_layers": [1, 2, 3]
}
def random_search(grid, n_iter=5, seed=42):
"""Perform random search over a hyperparameter grid."""
random.seed(seed)
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ML engineering pipelines — Python code examples
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This page collects ml engineering pipelines snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
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Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.