CodeSnips

python

import mlflow
import mlflow.sklearn
from sklearn.metrics import roc_auc_score

mlflow.set_experiment('customer-churn')

Experiment tracking and model registry workflows with MLflow

mlflow experiment-tracking model-registry

by Dr. Elena Vasquez 1 tab

python

import joblib
import pandas as pd
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI(title='Churn Prediction API')

Serving scikit-learn models behind a FastAPI prediction API

fastapi scikit-learn model-serving

by Dr. Elena Vasquez 1 tab

python

import joblib
from skl2onnx import to_onnx
from skl2onnx.common.data_types import FloatTensorType

joblib.dump(model, 'artifacts/model.joblib')

Serializing models with joblib, pickle, and ONNX tradeoffs

model-serialization joblib onnx

by Dr. Elena Vasquez 1 tab

python

import great_expectations as gx

context = gx.get_context()
data_source = context.data_sources.add_pandas(name='training_data')
asset = data_source.add_dataframe_asset(name='churn_asset')
batch_definition = asset.add_batch_definition_whole_dataframe('full_dataframe')

Great Expectations checks for dataset health before retraining

great-expectations data-quality mlops

by Dr. Elena Vasquez 1 tab