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During its online Virtual Global Inspire conference today, Alteryx, which offers an analytics automation platform, announced it has added an Alteryx Machine Learning tool to its portfolio, along with several other features.

Based on the AutoML framework originally developed by Microsoft, Alteryx Machine Learning offers business analysts and data scientists a guided approach to building AI models via an early access program. It also provides access to a patent-pending implementation of Deep Feature Synthesis (DFS), open source software for building artifacts for AI models known as features from multiple datasets. As a concept, DFS was pioneered by the Massachusetts Institute of Technology (MIT).

Alteryx Machine Learning also includes a built-in Feature Store repository for managing those artifacts, an Automated Insight Generation capability that makes it simpler to uncover hidden factors and relationships within datasets, and integration with an Alteryx Designer tool to automate data preparation tasks that come with more than 300 reusable building blocks and include integrations with a wide range of external data sources.

The company has also launched Alteryx Intelligence Suite, which combines natural language processing (NLP), text mining, and computer vision tools with Alteryx Designer to make it simpler to incorporate semi-structured, image-based data. Alteryx Designer, which now provides access to a unified software development kit and application programming interface (API), is also being made available in preview via a cloud-based service dubbed Alteryx Designer Cloud.


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Alteryx has also launched an online community that can be directly accessed from within the Alteryx Analytic Process Automation Platform. And yesterday the company revealed it has set up Alteryx Ventures, a $50 million fund that will invest in companies developing technologies that complement and expand the Alteryx analytics and data science portfolio.


The overarching goal of these initiatives is to make data science tools more accessible to the business analysts, domain experts, and data scientists who increasingly need to collaborate, Alteryx chief data and analytics officer Alan Jacobson told VentureBeat. The set of low code/no code tools Alteryx provides makes it easier for teams of individuals with varying degrees of data science expertise to collaborate, he added.

This is critical at a time when machine learning algorithms are being infused into almost every process, Jacobson said. “There is already AI or ML in almost every facet of our lives,” he noted.

The challenge is that data scientists typically don’t have a lot of industry domain knowledge, while line-of-business users with that expertise very often still rely on spreadsheets to analyze data, Jacobson said. But the rise of frameworks such as AutoML should finally convince many end users to replace those spreadsheets with more robust AI-infused analytics applications.

Alteryx, of course, is not the only provider of analytics applications racing to embed AI capabilities within various tools and platforms. However, as the AI capabilities within these applications become more accessible, their user base should expand well beyond the business analysts that have historically been the primary users of these applications. In fact, the number of users making fact-based decisions faster rather than continuing to rely solely on gut instinct informed by personal experience should steadily decline.

It’s not clear how much of a generational divide will exist between end users of analytics applications infused with AI and those who prefer spreadsheets, although there are currently a lot more of the latter than the former. But with each wave of college graduates, the number of individuals who prefer spreadsheets continues to decline.

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