We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for solving a variety of complex problems on their own. This versatility to solve diverse problems is achieved by transforming complex Spatio-temporal datasets to make them consumable by standard deep learning models, in this case, Convolutional Neural Networks (CNNs), and giving the ability to formulate disparate problems in a standard way, eg. With an intuitive user interface, a feature store that hosts derivatives of complex feature engineering, a deep learning kernel, and a scalable data processing mechanism, Trinity provides a powerful platform for domain experts to share the stage with scientists and engineers in solving business-critical problems. It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment. In this paper, we present our motivation behind Trinity and its design along with showcasing sample applications to motivate the idea of lowering the bar to using AI.

Artificial intelligence (AI) is a cutting-edge technology that is being adopted by forward-thinking businesses. The concept of artificial intelligence, on the other hand, has been around for decades. In 1955, "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" was published, which coined the term "artificial intelligence." Dartmouth University sponsored the first AI research project in 1956, which is widely regarded as the start of artificial intelligence. So, why is AI gaining popularity now, more than sixty years later?

This article is part of our series that explores the business of artificial intelligence. The growing digitization of nearly every aspect of our world and lives has created immense opportunities for the productive application of machine learning and data science. Organizations and institutions across the board are feeling the need to innovate and reinvent themselves by using artificial intelligence and putting their data to good use. And according to several surveys, data science is among the fastest-growing in-demand skills in different sectors. However, the growing demand for AI is hampered by the very low supply of data scientists and machine learning experts.

The growing digitization of nearly every aspect of our world and lives has created immense opportunities for the productive application of machine learning and data science. Organizations and institutions across the board are feeling the need to innovate and reinvent themselves by using artificial intelligence and putting their data to good use. And according to several surveys, data science is among the fastest-growing in-demand skills in different sectors. However, the growing demand for AI is hampered by the very low supply of data scientists and machine learning experts. Among the efforts to address this talent gap is the fast-evolving field of no-code AI, tools that make the creation and deployment of ML models accessible to organizations that don't have enough highly skilled data scientists and machine learning engineers.

With the rise of digital transformation and advancements in machine learning (ML), an increasing number of businesses start incorporating artificial intelligence (AI) into their workflow. In research by Deloitte, a considerable number of responders said that cognitive technologies are either "important" or "very important" to both service offerings and internal business processes. Apparently, these companies feel that using AI is central to their ability to develop their businesses and get ahead of their competition. And because the demand for integrative AI is only growing, platforms suggesting no-code or low-code AI development grow in their number as well. This article will define the terms "low-code" and "no-code", will discuss the advantages and disadvantages of this technology, and introduce a list of low-code/no-code platforms for AI and computer vision.

Low code/ no-code platforms are a type of visual software that enable businesses and developers to drag and drop applications, connecting them to great apps. Low code/no-code approaches allow developers to quickly build applications and alleviate the need to write codes line by line. This helps small business owners, office administrators, business analysts and others who are not well versed with software development to develop test applications. These people have little or no knowledge of programming, development work or machine code. Programmers write lines of code to generate the capabilities and features requested in a computer programme or application in traditional software development.

Technology often follows a familiar progression. First, it's used by a small core of scientists, then the user base expands to engineers who can navigate technical nuance and jargon until finally it's made user-friendly enough that almost anyone can use it. Right now, the process for building software is making that final leap. Just as the clickable icons of Windows and Mac OS replaced obscure DOS commands, new "no-code" platforms are replacing programming languages with simple drag and drop interfaces. The implications are huge: Where it used to require a team of engineers to build a piece of software, now users with a web browser and an idea have the power to bring that idea to life themselves.

The process of building and deploying artificial intelligence (AI) can be long and expensive. It leads organizations to spending an average of around $40 million a year on AI, while relatively few projects make it into production. Such Automated Machine Learning tools (AutoML) as Microsoft Azure can be a powerful way for technical AI professionals to speed up their workflows. Though, they are expensive, time-consuming, inaccessible to non- technical people and difficult to deploy and maintain. Let's explore these points in-depth to see why no-code AI platforms can be more efficient than traditional AI tools.

While a majority of AI projects still don't reach production, the interest for no-code AI platforms keeps rising. Indeed, a growing number of startups and large tech firms now propose "easy-to-use" ML platforms. The idea of being able to build and use a solution based on Machine Learning without being a data scientist is something very interesting for both small and large companies who could empower their employees while dedicating more resources to complex ML projects. In this article, I will share what have I learned after having implemented one of these no-code AI solutions and analyzed several startups related to this industry. As an AI consultant, my goal was to determine if these solutions could help us increase the chance of having more projects transitioning from proof of concepts (PoCs) to scalable, relevant, and efficient deployed AI solutions.

We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for solving a variety of complex problems on their own. This versatility to solve diverse problems is achieved by transforming complex Spatio-temporal datasets to make them consumable by standard deep learning models, in this case, Convolutional Neural Networks (CNNs), and giving the ability to formulate disparate problems in a standard way, eg. semantic segmentation. With an intuitive user interface, a feature store that hosts derivatives of complex feature engineering, a deep learning kernel, and a scalable data processing mechanism, Trinity provides a powerful platform for domain experts to share the stage with scientists and engineers in solving business-critical problems. It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment. In this paper, we present our motivation behind Trinity and its design along with showcasing sample applications to motivate the idea of lowering the bar to using AI.