Model deployment for real-time predictions is now available in Oracle Cloud Infrastructure Data Science

March 22, 2021 | 6 minute read
Tzvi Keisar
Director of Product Management
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On March 19, 2021, Oracle Cloud Infrastructure (OCI) Data Science released a new feature called Model Deployment to enable the serving of machine learning models as HTTP endpoints and provide real-time scoring of data. This feature is available in the OCI Software Development Kits (SDKs), OCI Command Line Interface (CLI), and OCI Console.

Machine learning, as complex as it may be, serves a business purpose, and that is using existing data to make predictions on future data. To meet that purpose, models that were trained on existing data need to be available to make predictions on fresh data that comes in.

We are excited to announce the release of model deployment, enabling models to be served as HTTP endpoints, receive requests, and send responses back with the model predictions in real time.

 

What does model deployment mean?

The machine learning journey does not end after training the model. Data science teams create models in order to generate predictions on fresh data. One of the most useful ways to do that is to wrap the model as a web service and enable it to accept HTTP requests with data to be scored. The web service then invokes the model using the request data and sends the output of the model prediction back to the requestor as HTTP response. This provides for data to be scored on demand in real time.

Machine learning model lifecycle

 

Deploying models in Oracle Cloud Infrastructure Data Science service

OCI Data Science provides a managed solution for deploying models at scale. The service comes with everything you can expect from a cloud service out-of-the-box:

Managed deployment

The service handles all the complex “wiring.” There is no need to take care of provisioning the compute, installing the web service, or anything related to the infrastructure. Everything is handled and optimized by the service.

Secure

With Oracle Cloud Infrastructure Identity and Access Management built-in to the service, all that’s left for you to do is define the groups and the policies for those groups to create and invoke deployment endpoints.

Easy and streamlined

By pulling models from the model catalog, along with their inference, you can have a deployed model ready with a few lines of code (from the SDK/CLI) or a few clicks of a button (from the OCI Console). The built-in load balancer will handle the web traffic automatically and efficiently.

Flexible

Choose from any of the available compute shapes to deploy on, all are fully managed so you don’t have to worry about provisioning. Need to change the compute? You can scale up or down without downtime.

Traceable

Analyze the logs to gain insights into what’s happening with the model’s scoring or debug issues with the deployment.

 

Deploy from code or from the OCI Console

To deploy models with a few clicks directly from the OCI Console, all you need is a registered model in the model catalog.

The new model deployment resource page with "Create Model Deployment" button

Figure 1 The new model deployment resource page with "Create Model Deployment" button

Create model deployment page. Select the model from the model catalog, select compute and logging, and deploy.

Figure 2 Create model deployment page. Select the model from the model catalog, select compute and logging, and deploy.

If you prefer code, you can use the OCI SDK to deploy a model with a few lines of code. You’ll also be able to customize the logging configuration to your preference.

Deploy model (this is a short version for illustration purpose, the full code is available in the service documentation)

# Create the model deploy configuration:
model_deploy_configuration = CreateModelDeploymentDetails(display_name=model_deployment_name,
                                                          description=description,
                                                          project_id=project_ocid,
                                                          compartment_id=compartment_ocid,
                                                          model_deployment_configuration_details=single_model_config,
                                                          category_log_details=logs_configuration_details_object)
  
# Creating a model deployment. This action takes a few minutes to complete.
data_science_composite = DataScienceClientCompositeOperations(data_science)
model_deployment = data_science_composite.create_model_deployment_and_wait_for_state(model_deploy_configuration,
                                                                                     wait_for_states=["SUCCEEDED", "FAILED"])

 

Automatic handling of HTTP traffic with load balancing

The managed service also handles the load balancing of incoming web traffic to make sure no single endpoint is overwhelmed with requests. The load balancer receives each request and routes it to the next available endpoint. This way, you get a continuous, reliable predictions service without the hassle of managing API gateways by yourself.

 

Manage the model deployment lifecycle

You are in control of the model deployment state. Once the deployment is ready, switch it to an active state from the OCI Console, the SDK, or the CLI.

When you want to pause the predictions service, simply deactivate it. The deployment is still ready but it no longer provides service to incoming requests. This also stops the billing for this deployment to save you costs.

 

Call the scoring service from anywhere

Once you have an active endpoint, you can send requests from any HTTP client.

Utilizing OCI Request Signatures for secure connection, you can use web clients such as Postman, Fiddler, or cUrl, or your own client application, for testing purposes.

Learn more about model deployment and how to call the endpoint in the service documentation.

 

Keep in touch! 

Tzvi Keisar

Director of Product Management

Product Manager in OCI Data Science


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