Manage a machine learning model using Azure Databricks

Training a machine learning model using Azure Databricks involves leveraging a unified analytics platform that provides a collaborative environment for data processing, model training, and deployment. Azure Databricks integrates with MLflow to manage the machine learning lifecycle, including experiment tracking and model serving.

This exercise should take approximately 20 minutes to complete.

Before you start

You’ll need an Azure subscription in which you have administrative-level access.

Provision an Azure Databricks workspace

Tip: If you already have an Azure Databricks workspace, you can skip this procedure and use your existing workspace.

This exercise includes a script to provision a new Azure Databricks workspace. The script attempts to create a Premium tier Azure Databricks workspace resource in a region in which your Azure subscription has sufficient quota for the compute cores required in this exercise; and assumes your user account has sufficient permissions in the subscription to create an Azure Databricks workspace resource. If the script fails due to insufficient quota or permissions, you can try to create an Azure Databricks workspace interactively in the Azure portal.

  1. In a web browser, sign into the Azure portal at https://portal.azure.com.

  2. Use the [>_] button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a PowerShell environment and creating storage if prompted. The cloud shell provides a command line interface in a pane at the bottom of the Azure portal, as shown here:

    Azure portal with a cloud shell pane

    Note: If you have previously created a cloud shell that uses a Bash environment, use the the drop-down menu at the top left of the cloud shell pane to change it to PowerShell.

  3. Note that you can resize the cloud shell by dragging the separator bar at the top of the pane, or by using the —, ◻, and X icons at the top right of the pane to minimize, maximize, and close the pane. For more information about using the Azure Cloud Shell, see the Azure Cloud Shell documentation.

  4. In the PowerShell pane, enter the following commands to clone this repo:

     rm -r mslearn-databricks -f
 git clone https://github.com/MicrosoftLearning/mslearn-databricks

  5. After the repo has been cloned, enter the following command to run the setup.ps1 script, which provisions an Azure Databricks workspace in an available region:

     ./mslearn-databricks/setup.ps1
  6. If prompted, choose which subscription you want to use (this will only happen if you have access to multiple Azure subscriptions).
  7. Wait for the script to complete - this typically takes around 5 minutes, but in some cases may take longer. While you are waiting, review the What is Databricks Machine Learning? article in the Azure Databricks documentation.

Create a cluster

Azure Databricks is a distributed processing platform that uses Apache Spark clusters to process data in parallel on multiple nodes. Each cluster consists of a driver node to coordinate the work, and worker nodes to perform processing tasks. In this exercise, you’ll create a single-node cluster to minimize the compute resources used in the lab environment (in which resources may be constrained). In a production environment, you’d typically create a cluster with multiple worker nodes.

Tip: If you already have a cluster with a 13.3 LTS ML or higher runtime version in your Azure Databricks workspace, you can use it to complete this exercise and skip this procedure.

  1. In the Azure portal, browse to the msl-xxxxxxx resource group that was created by the script (or the resource group containing your existing Azure Databricks workspace)
  2. Select your Azure Databricks Service resource (named databricks-xxxxxxx if you used the setup script to create it).

  3. In the Overview page for your workspace, use the Launch Workspace button to open your Azure Databricks workspace in a new browser tab; signing in if prompted.

    Tip: As you use the Databricks Workspace portal, various tips and notifications may be displayed. Dismiss these and follow the instructions provided to complete the tasks in this exercise.

  4. In the sidebar on the left, select the (+) New task, and then select Cluster.
  5. In the New Cluster page, create a new cluster with the following settings:
    • Cluster name: User Name’s cluster (the default cluster name)
    • Policy: Unrestricted
    • Cluster mode: Single Node
    • Access mode: Single user (with your user account selected)
    • Databricks runtime version: Select the ML edition of the latest non-beta version of the runtime (Not a Standard runtime version) that:
      • Does not use a GPU
      • Includes Scala > 2.11
      • Includes Spark > 3.4
    • Use Photon Acceleration: Unselected
    • Node type: Standard_D4ds_v5
    • Terminate after 20 minutes of inactivity
  6. Wait for the cluster to be created. It may take a minute or two.

Note: If your cluster fails to start, your subscription may have insufficient quota in the region where your Azure Databricks workspace is provisioned. See CPU core limit prevents cluster creation for details. If this happens, you can try deleting your workspace and creating a new one in a different region. You can specify a region as a parameter for the setup script like this: ./mslearn-databricks/setup.ps1 eastus

Create a notebook

You’re going to run code that uses the Spark MLLib library to train a machine learning model, so the first step is to create a new notebook in your workspace.

  1. In the sidebar, use the (+) New link to create a Notebook.
  2. Change the default notebook name (Untitled Notebook [date]) to Machine Learning and in the Connect drop-down list, select your cluster if it is not already selected. If the cluster is not running, it may take a minute or so to start.

Ingest and prepare the data

The scenario for this exercise is based on observations of penguins in Antarctica, with the goal of training a machine learning model to predict the species of an observed penguin based on its location and body measurements.

Citation: The penguins dataset used in the this exercise is a subset of data collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network.

  1. In the first cell of the notebook, enter the following code, which uses shell commands to download the penguin data from GitHub into the file system used by your cluster.

     %sh
 rm -r /dbfs/ml_lab
 mkdir /dbfs/ml_lab
 wget -O /dbfs/ml_lab/penguins.csv https://raw.githubusercontent.com/MicrosoftLearning/mslearn-databricks/main/data/penguins.csv

  2. Use the ▸ Run Cell menu option at the left of the cell to run it. Then wait for the Spark job run by the code to complete.

  3. Now prepare the data for machine learning. Under the existing code cell, use the + icon to add a new code cell. Then in the new cell, enter and run the following code to:

    • Remove any incomplete rows
    • Apply appropriate data types
    • View a random sample of the data
    • Split the data into two datasets: one for training, and another for testing.
    from pyspark.sql.types import *
from pyspark.sql.functions import *
   
data = spark.read.format("csv").option("header", "true").load("/hyperopt_lab/penguins.csv")
data = data.dropna().select(col("Island").astype("string"),
                          col("CulmenLength").astype("float"),
                          col("CulmenDepth").astype("float"),
                          col("FlipperLength").astype("float"),
                          col("BodyMass").astype("float"),
                          col("Species").astype("int")
                          )
display(data.sample(0.2))
   
splits = data.randomSplit([0.7, 0.3])
train = splits[0]
test = splits[1]
print ("Training Rows:", train.count(), " Testing Rows:", test.count())

Run a pipeline to preprocess the data and train a ML model

Before training your model, you need to perform feature engineering steps and then fit an algorithm to the data. To use the model with some test data to generate predictions, you have to apply the same feature engineering steps to the test data. A more efficient way to build and use models is to encapsulate the transformers used to prepare the data and the model used to train it in a pipeline.

  1. Use the following code to create a pipeline that encapsulates the data preparation and model training steps:

    from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, VectorAssembler, MinMaxScaler
from pyspark.ml.classification import LogisticRegression
   
catFeature = "Island"
numFeatures = ["CulmenLength", "CulmenDepth", "FlipperLength", "BodyMass"]
   
# Define the feature engineering and model training algorithm steps
catIndexer = StringIndexer(inputCol=catFeature, outputCol=catFeature + "Idx")
numVector = VectorAssembler(inputCols=numFeatures, outputCol="numericFeatures")
numScaler = MinMaxScaler(inputCol = numVector.getOutputCol(), outputCol="normalizedFeatures")
featureVector = VectorAssembler(inputCols=["IslandIdx", "normalizedFeatures"], outputCol="Features")
algo = LogisticRegression(labelCol="Species", featuresCol="Features", maxIter=10, regParam=0.3)
   
# Chain the steps as stages in a pipeline
pipeline = Pipeline(stages=[catIndexer, numVector, numScaler, featureVector, algo])
   
# Use the pipeline to prepare data and fit the model algorithm
model = pipeline.fit(train)
print ("Model trained!")

    Since the feature engineering steps are now encapsulated in the model trained by the pipeline, you can use the model with the test data without needing to apply each transformation (they’ll be applied automatically by the model).

  2. Use the following code to apply the pipeline to the test data and evaluate the model:

    prediction = model.transform(test)
predicted = prediction.select("Features", "probability", col("prediction").astype("Int"), col("Species").alias("trueLabel"))
display(predicted)

# Generate evaluation metrics
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
   
evaluator = MulticlassClassificationEvaluator(labelCol="Species", predictionCol="prediction")
   
# Simple accuracy
accuracy = evaluator.evaluate(prediction, {evaluator.metricName:"accuracy"})
print("Accuracy:", accuracy)
   
# Class metrics
labels = [0,1,2]
print("\nIndividual class metrics:")
for label in sorted(labels):
    print ("Class %s" % (label))
   
    # Precision
    precision = evaluator.evaluate(prediction, {evaluator.metricLabel:label,
                                                    evaluator.metricName:"precisionByLabel"})
    print("\tPrecision:", precision)
   
    # Recall
    recall = evaluator.evaluate(prediction, {evaluator.metricLabel:label,
                                             evaluator.metricName:"recallByLabel"})
    print("\tRecall:", recall)
   
    # F1 score
    f1 = evaluator.evaluate(prediction, {evaluator.metricLabel:label,
                                         evaluator.metricName:"fMeasureByLabel"})
    print("\tF1 Score:", f1)
   
# Weighed (overall) metrics
overallPrecision = evaluator.evaluate(prediction, {evaluator.metricName:"weightedPrecision"})
print("Overall Precision:", overallPrecision)
overallRecall = evaluator.evaluate(prediction, {evaluator.metricName:"weightedRecall"})
print("Overall Recall:", overallRecall)
overallF1 = evaluator.evaluate(prediction, {evaluator.metricName:"weightedFMeasure"})
print("Overall F1 Score:", overallF1)

Register and deploy the model

You’ve already logged the model trained by each experiment run when you ran the pipeline. You can also register models and deploy them so they can be served to client applications.

Note: Model serving is only supported in Azure Databricks Premium workspaces, and is restricted to certain regions.

  1. Select Experiments on the left pane.
  2. Select the experiment generated with your notebook’s name and view the details page for the most recent experiment run.
  3. Use the Register Model button to register the model that was logged in that experiment and when prompted, create a new model named Penguin Predictor.
  4. When the model has been registered, view the Models page (in the navigation bar on the left) and select the Penguin Predictor model.
  5. In the page for the Penguin Predictor model, use the Use model for inference button to create a new real-time endpoint with the following settings:
    • Model: Penguin Predictor
    • Model version: 1
    • Endpoint: predict-penguin
    • Compute size: Small

    The serving endpoint is hosted in a new cluster, which it may take several minutes to create.

  6. When the endpoint has been created, use the Query endpoint button at the top right to open an interface from which you can test the endpoint. Then in the test interface, on the Browser tab, enter the following JSON request and use the Send Request button to call the endpoint and generate a prediction.

     {
   "dataframe_records": [
   {
      "Island": "Biscoe",
      "CulmenLength": 48.7,
      "CulmenDepth": 14.1,
      "FlipperLength": 210,
      "BodyMass": 4450
   }
   ]
 }
  7. Experiment with a few different values for the penguin features and observe the results that are returned. Then, close the test interface.

Delete the endpoint

When the endpoint is not longer required, you should delete it to avoid unnecessary costs.

In the predict-penguin endpoint page, in the ⁝ menu, select Delete.

Clean up

In Azure Databricks portal, on the Compute page, select your cluster and select ■ Terminate to shut it down.

If you’ve finished exploring Azure Databricks, you can delete the resources you’ve created to avoid unnecessary Azure costs and free up capacity in your subscription.

More information: For more information see the Spark MLLib documentation.