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Multiple companies are vying for a piece of share in the competitive – and growing – low-code AI and ML tools market. The basic concept behind low code is about making it easier for users to build applications without needing to dig into source code development tools. Building out AI and ML integrations is a growing area and the space where San Jose, California-based Iterate is playing.
The company, which is releasing its Interplay 7 platform today, is firmly focused on AI. The release integrates a new workflow engine that enables users to work with multiple AI nodes to load data for building applications and machine learning models. It also provides streamlined integration with Google Vertex AI and AWS Sagemaker for users that want to send trained data models to those services for additional processing. The overall goal is to help larger companies with a modular approach to building AI/ML powered applications.
“We have realized that a lot of large companies we work with have many web engineers, but they actually don’t have machine learning backend skills,” Brian Sathianathan, CTO at Iterate told VentureBeat.
Competitive marketplace for low code shows promise for AI
The overall market for low-code development tools is forecast to be worth $16.8 billion in 2022 according to a Gartner research forecast. Gartner expects the total market for low code to reach $20.4 billion by 2023.
Looking out even further, Gartner has forecast that it expects a whopping 70% of all new applications developed by companies will use some form of low-code approach by 2025. In contrast, the analyst firm reported that in 2020 fewer than 25% of organizations were using low-code to build new applications.
The vendor marketplace for low-code technologies is crowded: Sstartup Sway.ai launched its no-code platform for helping users to build AI powered applications in February. In the same month, startup Mage launched its low-code AI dev tool which helps organizations to generate models. Cogni team announced its latest low-code AI updates on May 9, with a focus on robotics. Other established vendors in the low-code space include Appian and Mendixboth of whom have varying degrees of AI enablement capabilities.
“Low-code represents the primary growth area for AI-driven application development atop a complicated web of services and data sources,” Jason English, principal analyst at Intellyx LLC told VentureBeat† †Bringing down the technical barrier to entry allows business expertise to be applied for ML training, automation and inference tasks.”
How Iterate aims to differentiate
Brian Sathianathan, CTO at Iterate, told VentureBeat that among the ways his company’s platform differentiates against other low-code technologies in the marketplace is with specific industry use case templates†
For example, rather than just providing a generic tool to help users connect to data sources and build out an AI-enabled application, Interplay provides templates for industry verticals including retail, healthcare, automotive and oil and gas industries. Sathianathan said an organization will typically engage with Iterate to solve a specific use case, though the platform can also be customized to build out applications beyond the available templates as well.
In the Interplay 7 release, Iterative is also improving its data preparation capabilities with a visual tool to clean the data so that it is useful for machine learning training operations. In prior releases. Sathianathan said that data cleaning was not a visual process.
A primary theme with the Interplay update is to Improve the intersection of what end users see overall. It now integrates with the popular Figma tool that is used to mock up interface designs. Sathianathan said that at large organizations, customer-facing interfaces are often designed by agencies that, more often than not, use Figma. The integration enables Figma designs to be imported into Interplay 7, which can then be connected into the AI backend to build applications.
Looking forward to future releases, Sathianathan said he’s looking to expand the AI capabilities his platform can support for the development of AI techniques such as digital twins†
“Today we support the big four use cases of AI including regression, classification, clustering and image recognition,” he said. “In the future we’re going to add additional capabilities,” he said.
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