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Today, we are very excited to announce the public preview of two Alteryx Designer tools that connect to Azure Machine Learning automated ML (Machine Learning), allowing you to easily leverage the power of automated machine learning from inside Designer.
With the Azure Machine Learning Training tool, you can send data directly from Designer to be trained by Azure Machine Learning automated ML. Use the Azure Machine Learning Scoring tool to use the model you trained for predictions on new data.
With the debut of the Python tool in Alteryx Designer 2018.3, many great macros from PDF table extraction to Parquet file integration are born. As a citizen data scientist, the first instinct is to import some great scikit-learn package into the predictive palette. The question is where to start?
The hype around Artificial Intelligence (AI) reached a fevered pitch as the world entered 2019. But what does AI really mean? And how is it related to similarly mystifying terms like ML (machine learning) or ‘deep-learning’?
Have you struggled to deploy your predictive models in a timely manner before they become obsolete? This article will show you how Alteryx Promote solves this challenge by deploying your model into a RESTful API that can be called from a wide variety of enterprise applications.
There are two types of model errors when making an estimate; bias and variance. Understanding both of these types of errors, as well as how they relate to one another is fundamentally important to understanding model overfitting, underfitting, and complexity.