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Getting started

This page helps you get started with the H2O MLOps Python client by walking through a complete quickstart workflow, from connecting to H2O MLOps, to deploying a model, and scoring data against the deployment.

H2O MLOps enables teams to manage the lifecycle of machine learning models, including registration, deployment, monitoring, and scoring. The Python client allows you to perform these tasks directly from your Python code.

Follow these steps to connect to your H2O MLOps environment, create a project, register a model, deploy it, and score data against the deployment.

Prerequisites​

Before you begin, install the H2O MLOps Python client. For more information, see Installation.

Step 1: Import the required packages​

import h2o_mlops
import h2o_mlops.options as options
import h2o_mlops.types as types

Step 2: Initialize the H2O MLOps client​

Connect to H2O MLOps using your H2O Cloud URL, refresh token, and SSL certificate (if required):

mlops = h2o_mlops.Client(
   h2o_cloud_url=<H2O_CLOUD_URL>,
   refresh_token=<REFRESH_TOKEN>,
   ssl_cacert="/path/to/your/ca_certificate.pem",
)
note

Replace placeholders with your actual credentials and file paths. If SSL is not needed, you can omit ssl_cacert.

Step 3: Create a project​

Projects are containers that group related models, deployments, and artifacts. Create a new one:

project = mlops.projects.create(name="my-project")

Step 4: Register an experiment as a model version​

Register an existing experiment artifact as a model version to make it available for deployment:

model = project.models.register(
   experiment="/path/to/h2o3_mojo_glm_regression.zip",
   name="my-experiment",
)

Make sure the path points to a valid model artifact on your local machine.

Step 5: Deploy the model​

Deploy the registered model as an API endpoint using a supported scoring runtime:

deployment = project.deployments.create(
   name="my-deployment",
   composition_options=options.CompositionOptions(
       model=model,
       scoring_runtime=model.experiment().scoring_runtimes[0]
   ),
   security_options=options.SecurityOptions(
       security_type=types.SecurityType.DISABLED,
   ),
)

Step 6: Wait for the deployment to become healthy​

Deployment may take a few seconds. Use the following to wait until it's ready:

deployment.wait_for_healthy()

Step 7: Score data against the deployment​

Once the deployment is healthy, you can send data to it for scoring:

deployment.scorer.score(
   payload={
       "fields": [
           "Origin", "Dest", "fDayofMonth", "fYear", "UniqueCarrier", "fDayOfWeek", "fMonth", "IsDepDelayed",
       ],
       "rows": [
           ["text", "text", "text", "text", "text", "text", "text", "text"],
           ["text", "text", "text", "text", "text", "text", "text", "text"],
           ["text", "text", "text", "text", "text", "text", "text", "text"],
       ]
   },
)

The output contains predictions for the provided input rows:

{'id': '2afe0ab6-db1c-4ecc-abb2-747340b3b8dc',
'fields': ['Distance'],
'score': [['713.7770420135266'],
 ['713.7770420135266'],
 ['713.7770420135266']]}

Explore more examples​

This Getting started page covered the basics of connecting to H2O MLOps, deploying a model, and scoring data.

To learn more, check out the Examples section. It includes practical code samples for a wide range of operations, such as how to:

Each example is designed to help you understand how to use the H2O MLOps Python client for specific tasks in your workflow.

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