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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.

  • No-code AI
  • Low-code AI
  • Advantages and disadvantages
  • Use cases
  • List of platforms
  • Key thoughts
no-code/low-code artificial intelligence platforms

No-code development

No-code is an approach in software development enabling non-tech people, i.e. any person without prior training to design applications, websites, without manually writing code. No-code became even more popular during COVID-19 when businesses switching online did not have the time and resources to develop their own software, applications, eCommerce websites, etc, so they needed a quicker and more accessible way to integrate new solutions. When it comes to AI, a code-free system for deploying AI and ML models is defined as no-code AI. It facilitates data classification and analysis for AI models that serve specific business purposes.

Low-code development

Low-code goes hand in hand with no-code. Platforms of this type also help deliver apps faster but they may demand a little coding. Even experienced programmers often take advantage of these tools to avoid writing extra code. Anyhow, low-code and no-code tools are mostly targeted at business people and other professionals whose specialization in AI isn’t proficient enough to build models.

Where is low-code/no-code AI used?

Low-code/no-code AI can be used in any business to optimize workflows, predict churns and suggest recommendations. Simple image classification AI models can be easily developed with low-code/no-code platforms and used at factories to differentiate between quality and damaged products, or in the healthcare industry to detect if people are wearing masks inside the building. The use-cases are infinite.

Businesses will benefit from low-code/no-code AI platforms in more data-driven sectors, such as marketing, sales, and finance. AI can help in predicting churn rates, analyzing reports, adding smart suggestions, automating invoicing and much more.

Advantages of low-code/no-code platforms

Once we know there is an easier way to do something, we leverage it to get more efficient results in a shorter period of time. How do low-code/no-code AI platforms help us do that? Let’s take a look at the advantages.

  • Accessibility: Low-code/no-code platforms enable non-tech people or businesses to build AI systems from scratch, thus, making AI more accessible to a wider variety of companies.
  • Usability: These tools often have an intuitive drag-and-drop interface where the complexity is reduced to its minimum, so, as a rule, it’s quite easy to navigate through low-code/no-code AI platforms.
  • Speed: Because low-code/no-code AI platforms often have pre-built AI models, project templates, and ready-made datasets, it takes much less time to label and iterate the data, significantly accelerating the model development.
  • Scalability: AI performs tasks for a lot (if not a hundred) of users, saving time and resources of the company. Besides, the servers are automatically scaled up or down, depending on the load, and it’s really easy to follow the workload and the progress itself.

Disadvantages of low-code/no-code platforms

Though no-code/low-code AI platforms seem encouraging, there are still some drawbacks or obstacles to consider when choosing a platform.

  • Security: Some platforms may fail to design access protocols and that’s an issue for companies where security is at the utmost priority. It’s safe to research the terms and conditions to clearly understand how and where your data is going to be processed.
  • Lack of customization: Though easy and fast, low-code/no-code platforms are mostly limited in functionality, because they’re designed to cover a specific problem and it’s hard to come up with out-of-the-box, more complex solutions. Business needs change like the wind, so once you’ve outgrown a specific solution or functionality, where do you go next?
  • Requires consultation or training: Ideally, the ML engineer, human resources specialist, and marketing intern should be equally able to use the low-code/no-code platforms, but that’s not always the case. Because the end-user of an AI platform is an ML engineer anyways, it will take a lot of training and consultations for the rest of the team to find their way around AI processes.
  • Lack of trust: What we’ve seen so far is that low-code/no-code AI platforms gain popularity, but are they as practical as the traditional ML approaches? According to Google Trends, the interest in no code ML is increasing but people interested in traditional ML are far ahead. This is because ML and computer vision have been there for a while, these resources and libraries heavily outnumber low-code/no-code AI platforms.
no-code/low-code ml authority

Top 7 low-code and no-code AI platforms

Now that we’re familiar with the concepts of low-code and no-code platforms, let’s dive in and see what low-code/no-code AI resources exist out there. Low-code libraries such as PyCaret, H2O AutoML, and Auto-ViML can help ML engineers and AI enthusiasts to easily integrate AI into their business project. On top of that, we’re leaving a list of low-code/no-code AI platforms you can check out.

no-code/low-code ai platforms

Create ML

In iOS 11, Apple introduced the core ML framework for on-device ML predictions. This enabled apps to add ML  just by dragging in a trained model. A year later Apple introduced Create ML, which is a Mac OS framework that makes it easy for anyone to build ML models with an easy-to-use app interface and no code. Create ML can be used to train a variety of models for the following:

  • Image recognition
  • Sentiment analysis
  • Regression analysis

Google AutoML

AutoML enables developers with limited ML expertise to train high-quality models specific to their business needs. It’s basically a set of tools for different AI projects:


Levity is another no-code AI platform to train and build AI models. It focuses on image, text, and document classification and enables users to train custom models on their use-case-specific data. Custom models also include a human-in-the-loop option, which means the model asks for input where it is unsure and will automatically learn from interactions.

  • Classify images, texts and documents.
  • Automate data iteration.


Lobe is a relatively new but super easy model training app for image classification with object detection and data classification coming soon. With Lobe you can do the following operations:

  • Create a dataset and label images.
  • Automatically train a model with no prior configuration.
  • Use your model in any app due to easy exporting.

Obviously AI

Obviously AI is a no-code AI platform that helps build ML algorithms for data prediction. The platform enables users to take the bird’s-eye view on the existing data, understand it and draw conclusions. It also suggests ready-made datasets, so you can test it out and get predictions right away. Obviously AI can be used for a number of business use-cases:

  • Forecasting company revenue
  • Optimizing supply chain
  • Personalizing marketing campaigns


Founded in 2018, MakeML now positions itself as an app to create object detection and segmentation ML models without code. Promising enough, isn’t it? Designed for macOS developers, the platform also suggests free computer vision datasets to train neural networks in less time. With MakeML you can:

  • Create your own datasets.
  • Build custom ML models in a few clicks.
  • Integrate your model into your app.


bounding boxes SuperAnnotate

SuperAnnotate is a leading platform designed to build the highest quality training datasets for computer vision and NLP. With advanced tooling and QA, ML and automation features, data curation, robust SDK, offline access, and integrated annotation services, the platform enables ML teams to build incredibly accurate datasets 3-5x faster. SuperAnnotate features include (but are not limited to):

Key takeaways

Are low-code/no-code platforms useful and popular? Yes! Will they completely replace traditional ML and computer vision? No. Due to the fact that there is still a lot to explore in ML, AI, and computer vision, the custom AI model-building approach is far from being replaced. Besides, low-code and no-code platforms are quite limited in terms of functionality customization, whereas when you’re building AI from scratch, the sky is the limit, you’re free to build the architecture, functionality, or pipeline that fits your project best. On the other hand, such custom model building can be rather expensive and time-consuming. So, our best guess is to use low-code/no-code platforms to cover very specific actions of your pipeline to simplify and fasten up the processes.

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