Next step after training ml model?

dhruvrawat101

While using DSS, I took a dateset, prepared it, made some charts for visualization stuff, trained it using a ML model ,got certain predictions out of it with R2 score 0.95.

The question is what next?

I am using free version so i don't think deployment can be done in free version .Please help me to deploy it as a web based application so that i can showcase it to someone.

Please tell the next steps after training model ..

Andrey

Hi,

Once the model is trained in the Lab, you can deploy it to the flow. From there you can either use it for batch scoring (by using a Score recipe and a test dataset) or by creating an API.

Please refer to this page for more info about API creation:

https://doc.dataiku.com/dss/latest/apinode/first-service-apideployer.html#create-the-api-directly-from-the-flow

For example, once the endpoint is created in the design node, you can send the following request:

curl --location --request POST 'http://localhost:4750/public/api/v1/service_id/entrypoint_id/predict' \
--header 'Content-Type: application/json' \
--data-raw '{
"features": {
"PassengerId": "892",
"Pclass": "3",
"Name": "Kelly, Mr. James",
"Sex": "male",
"Age": "34.5",
"SibSp": "0",
"Parch": "0",
"Ticket": "330911",
"Fare": "7.8292",
"Embarked": "Q"
}
}'

Where the port can be found by clicking on "Actions":

service_id and entrypoint_id is whatever you specify at the creation and data is the JSON you want to score (in my case it's the Titanic dataset row)