A look at some of the top AI platforms that don’t require any programming is provided by AI Magazine, which democratizes access to technology for people all over the world.
The use of no-code AI platforms is growing.
Consequently, businesses no longer need to be specialists in data and analytics to leverage its power. These platforms also make it possible to incorporate AI into decision-making procedures and offer useful insights that help firms make strategic decisions.
Codeless AI, another name for no-code AI, seeks to democratize access to AI. It indicates that AI and machine learning models are implemented using a no-code development platform.
In light of this, as the world’s businesses continue to invest in AI technologies, AI Magazine examines some of the top no-code AI platforms.
10. PyCaret
PyCaret is an open-source, low-code machine-learning framework that automates machine-learning operations using Python. It is a comprehensive machine learning and model management tool that is intended to speed up the AI experiment cycle and increase developer productivity.
Among many other things, PyCaret provides low-code and data preparation solutions. The platform’s goal is to make machine learning accessible to everyone, even non-technical users who just want to do basic analysis.
Under an MIT license, the platform is entirely free and open-source.
9. DataRobot
Leading the way in value-driven AI, DataRobot provides a cooperative approach to AI by leveraging its open AI platform and knowledge to enhance and optimize customers’ business operations.
Its platforms are made to make the intricate process of developing AI solutions easier and more automated for people who are not familiar with coding.
With a no-code interface, users can create and customize AI-powered apps with DataRobot’s No-Code AI Apps. Consequently, not only can they assess their performance within DataRobot, but they can also activate essential DataRobot services without the need to create models.
8. Clarifai
The autonomous AI startup Clarifai was established in 2013. It specializes in computer vision, natural language processing, and voice recognition and was one of the earliest deep learning systems. It offers an AI framework for unstructured text, audio, video, and picture data.
The full AI lifecycle—which includes data preparation, model creation, testing, and evaluation—is covered by the Clarifai platform. Getting AI models or applications up and running without the need for code or machine learning operations (MLOps) is also helpful.
7. RunwayML
The goal of RunwayML, which was established in 2018, is to develop multimodal AI systems that will stimulate human creativity in the future. Without any prior coding knowledge, artists can utilize this platform to use machine learning capabilities straightforwardly.
Users can quickly train and implement AI models using RunwayML without requiring a deep understanding of code. Numerous AI applications are supported by the platform, such as object identification, style transfer, image synthesis, and natural language processing.
To create AI-driven outputs, users can upload their own data or select from a library of pre-existing models.
6. Polymer
Polymer is a platform for data security that inspects data using cutting-edge machine learning techniques. The platform contextualizes data to make it simple for users to recognize dangers and immediately reduce the risks associated with AI.
Without knowing how to write code, users may construct detailed dashboards, insert data into presentations, and generate data visualizations with its user-friendly business intelligence tool. Similarly, one of its features, PolyAI, is an integrated conversational AI assistant that interprets data and produces visuals on demand in real-time.
5. MakeML on Apple
Apple is a tech company that has produced a machine learning framework and tool called Apple CreateML. It is intended to let developers create and train their machine-learning models, and it is specifically made for the macOS and iOS operating systems.
With its intuitive UI, the CreateML platform seeks to make training and deploying machine learning models across Apple devices easier.
AI developers can use the platform to build and implement machine learning models that are customized to meet their unique requirements. They can fully utilize Apple’s technology and software by doing this.
4. AutoML by Google
Google Cloud offers a package of machine learning tools and services called Google AutoML, which is designed to make the process of creating and implementing unique machine learning models easier. The platform provides several features and tools that address various facets of machine learning workflows.
Interestingly, because code is not needed, AutoML also lets developers with little experience with machine learning train high-quality models tailored to their business requirements. Developers and users may create unique machine-learning models in only a few minutes.
3. Akkio
Akkio is a technology business that was founded in 2019 to make AI accessible to all users, regardless of skill level. Its machine learning platform without coding is intended to assist teams in modern sales, marketing, and finance in developing and implementing AI prediction models.
Akkio helps organizations realize the full potential of artificial intelligence (AI) without requiring complicated data or coding skills by fusing machine learning technology with an efficient, user-friendly cloud platform.
Additionally, it is one of the only AI data platforms designed with agencies in mind, helping them increase performance throughout the whole client engagement lifecycle.
2. EvidentlyAI
A platform developed by ObviouslyAI allows non-technical business analysts to make forecasts based on their past data quickly. This is intended to facilitate quicker decision-making for enterprises.
Users may quickly connect their data sources to ObviouslyAI and use the platform’s automated machine-learning features to build and implement prediction models. The entire model-building process, including data pretreatment, feature selection, algorithm selection, and hyperparameter tweaking, may then be automated by the platform.
Consequently, users can produce precise forecasts rapidly and without requiring human involvement.
1. Amazon SageMaker
A cloud machine learning platform called Amazon SageMaker was first introduced in 2017 to empower AI developers to build, train, and implement machine learning models in the cloud. Additionally, it makes it possible for developers to implement machine learning models on edge and embedded systems.
SageMaker is based on Amazon’s twenty years of experience in creating AI and machine learning applications for use in the real world, such as robotics, voice-activated devices, intelligent shopping, personalization, and product suggestions.
The platform’s goal is to make the process of creating and implementing machine learning models easier and faster. Because of this, engineers and data scientists who might not have specialized knowledge in technology management can more easily access AI.