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Next step after training ml model?

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Next step after training ml model?

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 ..


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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:

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":

Screenshot 2020-07-06 at 12.04.11.png


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)


Andrey Avtomonov
R&D Engineer @ Dataiku
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