This study proposes the design and implementation of a multimodal LLM-based Multi-Agent System (MAS) leveraging a No-Code platform to address the practical constraints and significant entry barriers associated with AI adoption in enterprises. Advanced AI technologies, such as Large Language Models (LLMs), often pose challenges due to their technical complexity and high implementation costs, making them difficult for many organizations to adopt. To overcome these limitations, this research develops a No-Code-based Multi-Agent System designed to enable users without programming knowledge to easily build and manage AI systems. The study examines various use cases to validate the applicability of AI in business processes, including code generation from image-based notes, Advanced RAG-based question-answering systems, text-based image generation, and video generation using images and prompts. These systems lower the barriers to AI adoption, empowering not only professional developers but also general users to harness AI for significantly improved productivity and efficiency. By demonstrating the scalability and accessibility of No-Code platforms, this study advances the democratization of AI technologies within enterprises and validates the practical applicability of Multi-Agent Systems, ultimately contributing to the widespread adoption of AI across various industries.

With this release of Swift, users may now benefit from the latest menu designer functionality, which makes it simple to further refine the menu to improve findability and provide a uniform experience across devices. Simply open your Swift App Designer Studio, click the Edit icon, scroll down, and tap the "click to configure" option. The menu Designer will display after you tap on it, and all you have to do is drag and drop items and change the names of your sub-menus according to your preference. This release of Swift, introduces the OCR functionality. Using the OCR control, end-users can convert the images into text using Optical Character Recognition and save time and increase accuracy in data entry.

Low-code and no-code platforms are used to build applications, websites, mobile apps, forms, dashboards, data pipelines, and integrations. No-code platforms help business users, sometimes termed citizen developers, to migrate from spreadsheets, extend beyond email collaborations, and transition from manual task execution to using tools and automations across departments. Low-code platforms are usually for technologists and provide ways to deliver and support software with little or no coding. "You have to remember low code is just a fancy term for abstraction. We are abstracting away non-essential elements in order to simplify the user experience," says Gordon Allott, President and CEO of K3.

Mastering machine learning is extremely difficult and time-consuming. Even if you grasp machine learning, building data models from scratch might take months, on top of everything else you need to accomplish. However, machine learning does not have to be limited to technical programmers. Analysts now have the power of data forecasts to help them move quicker, which means they can assist their businesses think creatively and proactively without blowing the budget. In this article, we will discuss what is No-CodeML, How it works, and advantages of using it in your machine learning project.

Traditional computer programming has a steep learning curve that requires learning a programming language, for example C/C, Java or Python, just to build a simple application such as a calculator or Tic-tac-toe game. Programming also requires substantial debugging skills, which easily frustrates new learners. The study time, effort and experience needed often stop nonprogrammers from making software from scratch. No-code is a way to program websites, mobile apps and games without using codes or scripts, or sets of commands. People readily learn from visual cues, which led to the development of "what you see is what you get" (WYSIWYG) document and multimedia editors as early as the 1970s.

Founders Rima Al Shikh and Shaima Ghafoor aim to democratize AI by making data easily accessible by providing no-code solutions to all users. Genbu is an all-in-one platform that automates your ML for a few clicks. Say goodbye to costly data centralization and lengthy AI production, because with Genbu smart algorithms are at hand. Youcan launch apps without ML code. The solution automates the entire ML lifecycle in just few clicks and does not require any coding knowledge from their end users which saves time and money on training costs as well.

Machine Learning has wholly integrated into our everyday life. Naturally, many aspiring would-be analysts want to learn the magic behind ML. However, the application of the machine learning process in analyzing data involves a framework of collecting data, creating/choosing a model, preparing, and training the data until predicting the outcome. All of which makes the entire process appear too technical, challenging, and laborious, leaving those who aren't fluent in software development feeling overwhelmed. However, No-code Machine Learning is changing this status quo.

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.

In the span of a few years, low-code and no-code platforms, which reduce the burden on professional developers to create certain apps, have matured vastly. They have become resources large enterprises increasingly explore to speed up app development, but can that carry over to new, more intensive needs such as AI app development in a substantial way? Gartner predicts that by 2025, some 70% of new apps developed by enterprises will use low-code or no-code resources. In 2020, less than 25% of new apps were developed through such means at enterprises. Further, Gartner also projects 75% of large enterprises by 2025 will use at least four low-code tools for IT app and citizen development.

As per a report by Gartner, through 2024, large organisations will triple the capacity of their existing Robotic Process Automation (RPA) portfolios. It also predicts that 90 per cent of large organisations globally will have adopted RPA in some form by 2022, as they will look to digitally empower critical business processes through resilience and scalability while recalibrating human labour and manual effort. UiPath is one of the biggest names in the field of RPA. UiPath's Most Valuable Professional (MVP) Award is the highest recognition to its community members for their outstanding contribution, innovation, and evangelism shown in the larger automation community. In an email interaction, Nisarg Kadam, UiPath MVP 2021, working as a Senior Consultant at WonderBotz and Sishira Mishra, UiPath MVP 2021, working as Automation Architect at HP, discuss how they started their RPA journey, how UiPath helped them in attaining and honing their skills and what can we expect in the RPA domain in the future.