Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. Web Services transformation

Transformations

Transformations

Machine Learning transformation example

Machine Learning transformation example

You're a data scientist at a popular video streaming site and you need to generate a report on which users are likely to cancel their subscription so the marketing team can target their upcoming ad campaign.
You built a machine learning model to predict how likely users are to cancel their subscription based on their watch history, content preferences, and other user data. Your company stores this user data in a cloud data lake. To generate the report, you'll create a mapping to read from the data lake, pass the data through the machine learning model, and filter out results that aren't important to the marketing campaign.
Before you create the mapping, you set up a REST endpoint for the machine learning model. You also create an API collection with a POST request in
Data Integration
.
Then, you create a mapping that includes a Machine Learning transformation and a Filter transformation. The following image shows an overview of the mapping: The mapping canvas shows a mapping that includes a Source transformation, Machine Learning transformation, Filter transformation, and Target transformation.
You configure the transformations in the following ways:
Machine Learning transformation
On the
Model
tab, select a POST request from your API collection as the operation and select the connection to the machine learning model. The following image shows the configured
Model
tab: The Model tab of the Machine Learning transformation shows a POST operation selected and a REST V3 connection to the machine learning model.
On the
Request Mapping
tab, verify that the mapping of the incoming field from the Source transformation to the request schema field is correct. If needed, manually map the fields. The following image shows the configured
Request Mapping
tab: The Request Mapping tab of the Machine Learning transformation properties shows one incoming field, named source_data, mapped to the request schema field, named data.
Filter transformation
Since the marketing team is only interested in users who are unlikely to renew their subscription, set the filter condition to filter for users who have a probability of renewing less than 50%. The following image shows the configured filter condition: The Filter tab of the Filter transformation properties includes a simple filter condition.
When you run the mapping, it reads user data from the cloud data lake. The Machine Learning transformation makes a REST API call to pass the data through the request schema fields to the machine learning model. The model generates predictions about each user's likelihood of renewing, and the Machine Learning transformation captures the predictions in the response schema fields. The Machine Learning transformation passes the results to the Filter transformation, which filters out users who are likely to renew and passes users who are unlikely to renew to the Target transformation. The Target transformation writes the results to the target file, which you can send over to the marketing team.

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