• Low-code and Auto-ML are ushering in a new era of AI democratization 

• Citizen and expert AI developers can use these tools to accelerate AI production timelines

• By putting the power to develop AI and ML models in the hands of business users, organizations can reduce AI bias and enhance enterprise decision making

• READ PART ONE: Are Citizen Developers the Key to Applied AI Success?

• READ PART TWO: Data Governance as the Engine Powering AI Success

Until recently, developing applied artificial intelligence (applied AI) applications required years of specialized training and experience. In addition, developing a single AI program can take months. In fact, according to Algorithmia’s “2020 State of Enterprise ML,” 40% of companies surveyed said it takes more than a month to develop a single machine learning (ML) model, 28% do so in eight to 30 days, while only 14% could do so in seven days or less.

However, emerging technology such as low-code AI development tools and automated machine learning (Auto-ML or AutoML) technologies are changing this. By automating many of the most time consuming aspects of AI development, these tools enable both experienced AI developers and, to a certain extent, citizen developers create production ready ML/AI models quickly and efficiently with minimal coding requirements. 

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What are low-code AI and AutoML tools?

*Image sourced from https://www.ericsson.com/en/blog/2020/10/democratizing-ai

Backing up a minute, as the name suggests, low-code (a.k.a. no-code) AI development tools are platforms that enable business professionals with minimal or no coding experience to build AI applications. AutoML tools, on the other hand, automate the end-to-end ML development pipeline from cultivating the raw dataset to the deployable machine learning model.

For example, using Appian’s low-code tool, business users can build their own AI-powered intelligent document processing (IDP) workflows. Veritone’s Automate Studio “orchestrates a diverse ecosystem of hundreds of best-of-breed, ready-to-deploy AI models to transform structured and unstructured data into actionable intelligence.” In other words, using a drag & drop interface, both of these tools enable users to easily build automated workflows and integrate resulting insights into other software applications (such as RPA and intelligent automation) and business processes at scale. 

Though, with the proper training, both tools can be leveraged by non-technical business developers to create their own predictive analytics models or automated workflows, they still require significant expert oversight and IT involvement. Though dubbed as low-code to no code, most still require some coding to “finalize” the programming.

In fact, low-code and AutoML tools are often used by regular developers simply looking to accelerate the programming process. They also enable the reuse of successful code, workflows, algorithms, parameter tuning and analytics/ML models across the enterprise. 

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How do low-code AI and AutoML tools drive AI democratization?

First and foremost, low-code and automated development tools significantly reduce technical barriers for entry. Leveraging an easy to use drag & drop interface and pre-built algorithms, users from a wide range of backgrounds can experiment with and model AI applications that align with their/their teams business priorities. In the long-run, this helps users get more accustomed to using AI in their everyday work lives and, as they know firsthand the value these tools bring to the table, helps ensure buy-in for whatever the next big innovation comes next. 

In addition to democratizing AI within an enterprise, low-code and AutoML tools also have the potential to make AI more accessible to all types of businesses and organizations. Historically speaking, only the largest, richest companies and institutions were able to deploy AI given the immense cost and manpower required to do so. By making AI easier and cheaper to deploy, SMEs can now also start leveraging AI not to mention more budget-conscious or technologically limited countries, governments, schools and NGOs.

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In summary…...

Beyond breaking down technical barriers, low-code and AutoML platforms help strengthen IT/business alignment by incentivizing cross-functional collaboration and increasing transparency. By equipping business users with immediate (or close to it) access to advanced analytics tools, they also enhance enterprise decision making and promote wide-spread data democratization. In addition they also reduce AI bias by inherently increasing the number of people (and perspectives) involved in the coding process. 

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