In a previous post (Shopping recommendations in PMML) we built the main infrastructure for making personalized shopping recommendations depending on the customer shopping history. The companion project was meant to demonstrate a standalone implementation of the Trusty-PMML engine.
In this post, we will show how to invoke the engine on a remote Kogito instance.
Kogito is designed from the ground up to run at scale on cloud infrastructure, which means it is dedicated to developing cloud-native applications. A Kogito service may be deployed as a standalone microservice or inside a container, like Kubernetes. Besides that, its native compilation makes it extremely fast.
The overall architecture and design of theKogito environment are out of the scope of the current post: the reader may find full documentation here.
A detailed explanation of how to create and run a Kogito service is out of scope from the current post. Again, the reader may refer to official documentation. Simply put, the following three steps are required:
- create a Maven project with Kogito dependencies
- add the model file (cluster_buyer_predictor.pmml in our case) in the “resources folder
- start the Kogito service (we will use Quarkus environment in this post)
We will call the project "kogito-pmml-recommender", and sources are available here.
After issuing the command
mvn clean compile quarkus:dev
to service will be up and running, listening on port 8080. The endpoint for our model will be "KMeans" (the name of the model as defined inside cluster_buyer_predictor.pmml)
It is possible to use the REST API to actually invoke the running engine, sending a POST request. The JSON content of such a post should contain the input data for the Trusty-PMML engine. As we may recall from the previous post, such data is a 30-sized array of doubles with only "0.0" or "1.0". This is an excerpt of that json:
Request details are:
and curl equivalent is:
curl -X POST -H 'Accept: application/json' -H 'Content-Type: application/json' --data @commands/ClusterBuyerPredictor.json 'http://localhost:8080/KMeans
(the above curl invocation requires a "ClusterBuyerPredictor.json" file with the content listed before).
Kogito services are exposed as standard REST endpoints, so the java client application simply needs to use an HTTP Client.
The kogito-pmml-recommender-client project is a modified version of the recommender-engine we saw in the previous post, with the only difference being that it features the HTTP client to retrieve predictions with a remote invocation to the Kogito service. As a matter of fact, there are only a couple of required modifications:
- fix dependencies inside pom, i.e. remove compilation-related ones
- modify the PMMLUtils class to use the http client to make a remote invocation.
In this post, we have tackled a real-world recommendation scenario using the PMML and Trusty-PMML engine inside a Kogito microservice.
First, we have created a Kogito service project, basically containing only the cluster_buyer_predictor.pmml.
Then, we have provided a brief explanation on how to make a simple HTTP request to it.
Last, we have shown a bare-bone java project that, featuring a standard java HTTP client, is able to provide reliable recommendations.
But this is just to scratch the potentialities that the couple Trusty PMML + Kogito may offer.