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This document represents the customer-facing guidance, proposed by Alteryx product and engineering, related to log4j vulnerabilities and associated security findings. It provides context on the issue and recommended the next steps for customers to fix.
If you have a model that takes longer than 10 seconds to return results, by default Promote will time out your model API query. If you would like Promote to wait longer than 10 seconds before timing out, you can adjust this timeout setting with an environmental variable called PREDICTION_TIMEOUT.
One of the most important features of Promote is its ability to return near-real-time predictions from deployed models. Here is a list of frequently asked questions relating to Promote prediction requests.
Promote is data science model hosting and management software that allows its users to seamlessly deploy their data science models as highly available microservices that return near-real-time predictions by leveraging REST APIs. In this article, we provide an overview of Promote’s technical requirements and architecture.
When making a call to a Promote model, the input data used to make a prediction is sent in a JSON format. When working with an R model, prior to reaching the model.predict() function, the JSON string that was sent to your model is converted to an R format (either an R dataframe or an R list). By default, this conversion is performed with the fromJSON() function in the jsonlite R package.
This article outlines the process of restoring the Promote PostgreSQL database from a backup. For instructions on creating a backup, please see How To: Create a Promote PostgreSQL Database Backup.
Use these steps only if there is no data in your database.
Promote uses a PostgreSQL database. This article explains how to create a backup of a Promoteinstance's database. Backing up your Promote database can save you time and headaches in the event of a system failure.
Models deployed to Promote can be queried through a couple of different ways, one of them being a standard REST API post request. Querying a model consists of sending in the predictor variables to the model, allowing the model to process the data and make predictions. After the prediction is made from the model, the return is the score based on the predictor variables entered.
Welcome to part 2 of the Supporting Promote series. In this series, we will tackle some common issues and questions, and provide best practices for troubleshooting. In this article, we will be investigating one common "Promote Service Down" scenario - when the promote_logspout and promote_logstash services are down. You can follow these same steps to start troubleshooting other downed services.
Welcome to part 4 of the Supporting Promote series. In this series, we will tackle some common issues and questions, and provide best practices for troubleshooting. This article will demonstrate backing up and restoring your Promote PostgreSQL database.
Welcome to part 3 of the Supporting Promote series. In this series, we will tackle some common issues and questions, and provide best practices for troubleshooting. This article will step through the process of restoring the Promote web app.
Some Promote customers have run into an issue where the status of the predictive model will flicker between online and offline continuously on the Promote UI page. This article discusses the cause of the issue, as well as how to resolve it.