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Prediction Framework, a time saver for Data Science prediction projects



Posted by Álvaro Lamas, Héctor Parra, Jaime Martínez, Julia Hernández, Miguel Fernandes, Pablo Gil

Acquiring high value customers using predicted Lifetime Value, taking specific actions on high propensity of churn users, generating and activating audiences based on machine learning processed signals…All of those marketing scenarios require of analyzing first party data, performing predictions on the data and activating the results into the different marketing platforms like Google Ads as frequently as possible to keep the data fresh.

Feeding marketing platforms like Google Ads on a regular and frequent basis, requires a robust, report oriented and cost reduced ETL & prediction pipeline. These pipelines are very similar regardless of the use case and it’s very easy to fall into reinventing the wheel every time or manually copy & paste structural code increasing the risk of introducing errors.

Wouldn’t it be great to have a common reusable structure and just add the specific code for each of the stages?

Here is where Prediction Framework plays a key role in helping you implement and accelerate your first-party data prediction projects by providing the backbone elements of the predictive process.

Prediction Framework is a fully customizable pipeline that allows you to simplify the implementation of prediction projects. You only need to have the input data source, the logic to extract and process the data and a Vertex AutoML model ready to use along with the right feature list, and the framework will be in charge of creating and deploying the required artifacts. With a simple configuration, all the common artifacts of the different stages of this type of projects will be created and deployed for you: data extraction, data preparation (aka feature engineering), filtering, prediction and post-processing, in addition to some other operational functionality including backfilling, throttling (for API limits), synchronization, storage and reporting.

The Prediction Framework was built to be hosted in the Google Cloud Platform and it makes use of Cloud Functions to do all the data processing (extraction, preparation, filtering and post-prediction processing), Firestore, Pub/Sub and Schedulers for the throttling system and to coordinate the different phases of the predictive process, Vertex AutoML to host your machine learning model and BigQuery as the final storage of your predictions.

Prediction Framework Architecture

To get involved and start using the Prediction Framework, a configuration file needs to be prepared with some environment variables about the Google Cloud Project to be used, the data sources, the ML model to make the predictions and the scheduler for the throttling system. In addition, custom queries for the data extraction, preparation, filtering and post-processing need to be added in the deploy files customization. Then, the deployment is done automatically using a deployment script provided by the tool.

Once deployed, all the stages will be executed one after the other, storing the intermediate and final data in the BigQuery tables:

  • Extract: this step will, on a timely basis, query the transactions from the data source, corresponding to the run date (scheduler or backfill run date) and will store them in a new table into the local project BigQuery.
  • Prepare: immediately after the extract of the transactions for one specific date is available, the data will be picked up from the local BigQuery and processed according to the specs of the model. Once the data is processed, it will be stored in a new table into the local project BigQuery.
  • Filter: this step will query the data stored by the prepare process and will filter the required data and store it into the local project BigQuery. (i.e only taking into consideration new customers transactionsWhat a new customer is up to the instantiation of the framework for the specific use case. Will be covered later).
  • Predict: once the new customers are stored, this step will read them from BigQuery and call the prediction using Vertex API. A formula based on the result of the prediction could be applied to tune the value or to apply thresholds. Once the data is ready, it will be stored into the BigQuery within the target project.
  • Post_process: A formula could be applied to the AutoML batch results to tune the value or to apply thresholds. Once the data is ready, it will be stored into the BigQuery within the target project.

One of the powerful features of the prediction framework is that it allows backfilling directly from the BigQuery user interface, so in case you’d need to reprocess a whole period of time, it could be done in literally 4 clicks.

In summary: Prediction Framework simplifies the implementation of first-party data prediction projects, saving time and minimizing errors of manual deployments of recurrent architectures.

For additional information and to start experimenting, you can visit the Prediction Framework repository on Github.

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