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Deploying Python functions in Watson Machine Learning

Deploying Python functions in Watson Machine Learning

You can deploy Python functions in Watson Machine Learning the same way that you can deploy models. Your tools and apps can use the watsonx.ai Python client or REST API to send data to your deployed functions the same way that they send data to deployed models. Deploying Python functions gives you the ability to hide details (such as credentials). You can also preprocess data before you pass it to models. Additionally, you can handle errors and include calls to multiple models, all within the deployed function instead of in your application.

Sample notebooks for creating and deploying Python functions

For examples of how to create and deploy Python functions by using the watsonx.ai Python client library, refer to these sample notebooks:

Sample name Framework Techniques demonstrated
Use Python function to recognize hand-written digits Python Use a function to store a sample model and deploy it.
Predict business for cars Hybrid(Tensorflow) Set up an AI definition
Prepare the data
Create a Keras model by using Tensorflow
Deploy and score the model
Define, store, and deploy a Python function
Deploy Python function for software specification Core Create a Python function
Create a web service
Score the model

The notebooks demonstrate the six steps for creating and deploying a function:

  1. Define the function.
  2. Authenticate and define a space.
  3. Store the function in the repository.
  4. Get the software specification.
  5. Deploy the stored function.
  6. Send data to the function for processing.

For links to other sample notebooks that use the watsonx.ai Python client library, refer to Using Watson Machine Learning in a notebook.

Increasing scalability for a function

When you deploy a function from a deployment space or programmatically, a single copy of the function is deployed by default. To increase scalability, you can increase the number of replicas by editing the configuration of the deployment. More replicas allow for a larger volume of scoring requests.

The following example uses the Python client API to set the number of replicas to 3.

change_meta = {
                client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {

client.deployments.update(<deployment_id>, change_meta)

Learn more

Parent topic: Managing predictive deployments

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