Understanding no-code AI and the best no-code machine learning (ML) tools for some DIY ML projects
AI applications have already made their way into almost every industry; companies do not adopt them. According to Forbes, 83% of companies believe AI is a strategic priority for them, but there is a shortage of qualified data scientists. This is not only because AI solutions and expertise are expensive, but also because companies lack the infrastructure to support these solutions.
Companies are increasingly deploying AI and machine learning models using no-code AI, a no-code development platform with a visual, no-code, and typically drag-and-drop interface. Non-technical people can quickly classify, evaluate, and develop accurate models to make predictions without coding AI.
There is no code. Individuals and businesses can now experiment with AI and machine learning more effectively. These solutions help companies quickly and cost-effectively adopt AI models, empowering their domain experts with cutting-edge technology.
No-code machine learning offers many benefits such as:
- It helps businesses save money by automating processes. When companies can ask their business users to build machine learning models, they need fewer data scientists.
- No-code solutions reduce the number of requests for data scientists to perform simple tasks by allowing business users to manage these requests themselves.
- Writing code, cleaning data, categorizing, structuring data, training and debugging the model are all necessary steps in creating unique AI solutions. According to studies, solutions with or without code can save 90% of development time.
Above all, no-code AI helps business users leverage their domain-specific experience and quickly build AI solutions.
Some of the no-code machine learning tools are mentioned below:
This platform offers vision (image classification), natural language, AutoML translation, video intelligence and tables among machine learning solutions. By offering out-of-the-box support for widely validated deep learning models, AutoML on the cloud eliminates the need to know transfer learning or neural network design. This allows developers with little or no machine learning experience to train models tailored to their specific use cases.
It is a development tool that allows users to create object detection and semantic segmentation models without writing a single line of code. It offers iOS developer macOS software for creating and managing datasets (such as annotating objects in images). They also have a dataset store with free computer vision datasets that can be used to train a neural network in seconds.
This is another machine learning platform from Google that does not require any coding. Teachable Machines allows easy training of models to identify sights, sounds, and poses right in your browser. Users can teach your model by simply dragging and dropping files. They can also use the webcam to create a quick and dirty data set of images or sounds.
Apart from training models, data processing takes a lot of time when building machine learning projects. SuperAnnotate is an AI-powered annotation software that supercharges your data annotation process using machine learning skills. Users can annotate data instantly using their image and video annotation tools, including built-in prediction models.
This no-code platform offers tools to create synthetic photos and videos, edit material, and animate faces using AI. The company’s platform, designed for creatives, lets them use machine learning to streamline their work.
BRYTER is a service automation company that helps businesses build virtual assistants, chatbots, self-service tools, and other applications. The company’s platform offers a scalable compliance solution for legal, tax, HR and security services, which are often done manually and difficult to scale.
Although no-code AI platforms cannot completely replace a team of trained IT professionals, they offer a viable alternative for companies wishing to automate parts of their robotic (RPA) processes. While most no-code applications are currently confined to pre-made sets of industry-agnostic operations, as these companies build increasingly robust plug-and-play AI algorithms, the depth and range of options are expanding.