Alteryx Promote Knowledge Base

An overview of user management in Promote. 

Promote uses the application NGINX as a load balancer. In Promote, NGINX is configured to require TLS (Transport Layer Security) or SSL (Secure Sockets Layer) certificates. This article goes through the step by step process of using your own TLS/SSL certificate during installation or updating your TLS/SSL certificates after installation.

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.

If you’ve never heard of Docker, or aren’t particularly familiar with it, you are probably wondering “what’s the deal with Docker?”

There are two tools in Alteryx Designer that connect to Promote; the Deploy tool and the Score tool. The Deploy tool allows you to send trained models from Alteryx Designer to Promote. The Score tool allows you to connect to a model that has already been deployed to Promote to create predictions from a provided data set.

Did you know that you can use Promote to query a database (or include a database query in your Promote model)? 

This article provides an overview of the administrative options in Promote (excluding user management, which can be found here).

Keras is an open-source neural network API library, written in Python designed to run on top of TensorFlow, CNTK, or Theano. In this article, we demonstrate how to deploy a Keras model to Promote.

A Guide to your First Promote Model

examples of code-friendly models: R and Python

Welcome to the Supporting Promote series. In this series, we will tackle some common issues and questions, and provide best practices for troubleshooting your Promote instance.

Often, when deploying a model up to Promote, the model requires certain dependencies to run. These dependencies can be certain functions, files, etc. If your model requires them, you’ll need to create a promote.sh, which contains commands to import these dependencies.

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.

Although the Admin page indicates that there are models deployed to the Promote instance, the models are not appearing in the Promote UI home page.

Troubleshooting steps for missing model logs.

Use these instructions to replace an expired credential that causes errors such as unauthorized: Could not find robot with username.

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.