Using an MLflow Model Artifact in a Model REST API

You can use MLflow to create, deploy, and manage models as REST APIs to serve predictions

1. To create an MLflow model add the following information when you run an experiment:

   mlflow.log_artifacts ("output")
   mlflow.sklearn.log_model(lr, "model")

   For example:

   import os
   import warnings
   import sys
   import mlflow
   import pandas as pd
   import numpy as np
   from sklearn.metrics import mean_squared_error,
   mean_absolute_error, r2_score
   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import ElasticNet
   import mlflow.sklearn
   
   import logging
   
   logging.basicConfig(level=logging.WARN)
   logger = logging.getLogger(__name__)
   
   def eval_metrics(actual, pred):
       rmse = np.sqrt(mean_squared_error(actual, pred))
       mae = mean_absolute_error(actual, pred)
       r2 = r2_score(actual, pred)
       return rmse, mae, r2
       
   if __name__ == "__main__":
       mlflow.set_experiment("wine-quality-test")
       csv_url = (
   "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
       )
       try:
            data = pd.read_csv(csv_url, sep=";")
            except Exception as e:
            logger.exception(
                "Unable to download training & test CSV, check your
   internet connection. Error: %s", e
            )
            
   # Split the data into training and test sets. (0.75, 0.25)
   split.
       train, test = train_test_split(data)
   # The predicted column is "quality" which is a scalar from [3, 9]
       train_x = train.drop(["quality"], axis=1)
       test_x = test.drop(["quality"], axis=1)
       train_y = train[["quality"]]
       test_y = test[["quality"]]
       alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
       l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
       with mlflow.start_run():
           lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio,
           random_state=42)
           lr.fit(train_x, train_y)
           predicted_qualities = lr.predict(test_x)
           (rmse, mae, r2) = eval_metrics(test_y,
           predicted_qualities)
           print("Elasticnet model (alpha=%f, l1_ratio=%f):" %
           (alpha, l1_ratio))
           print(" RMSE: %s" % rmse)
           print(" MAE: %s" % mae)
           print(" R2: %s" % r2)
           mlflow.log_param("alpha", alpha)
           mlflow.log_param("l1_ratio", l1_ratio)
           mlflow.log_metric("rmse", rmse)
           mlflow.log_metric("r2", r2)
           mlflow.log_metric("mae", mae)
           mlflow.sklearn.log_model(lr, "model")

   In this example we are training a machine learning model using linear regression to predict wine quality. This script creates the MLflow model artifact and logs it to the model directory: /home/cdsw/.experiments/<experiment_id>/<run_id>/artifacts/models

2. To view the model, navigate to the Experiments page and select your experiment name. CML displays the Runs page and lists all of your current runs.
3. Click the run from step 1 that created the MLflow model. CML displays the Runs detail page
4. Click Artifacts to display a list of all the logged artifacts for the run.

   

5. Click model. CML displays the MLflow information you use to create predictions for your experiment.