AI Magazine considers some of the leading AI platforms that do not require a single line of code, helping to democratise global access to the technology
No-code AI platforms are on the rise.
As a result, companies can rely on the power of data and analytics without needing to be experts on it. These platforms also enable AI to be integrated into decision-making processes and provide actionable insights that guide strategic options for businesses.
No-code AI, also known as codeless AI, aims to democratise access to AI. It means that a no-code development platform is used to deploy AI and machine learning models.
With this in mind, AI Magazine considers some of the leading no-code AI platforms as the global business landscape continues to invest in AI technologies.
10. PyCaret
An open-source, low-code machine learning library, PyCaret operates in Python and automates machine learning workflows. It is an end-to-end machine learning and model management tool that is designed to make the experiment cycle of AI faster, thereby making the developer more productive.
PyCaret offers low-code and data preprocessing solutions, amongst numerous other functions. The platform aims to democratise machine learning for all, for both technical experts and those wishing to perform simple analyses.
The platform is completely free and open-source, licensed under MIT.
9. DataRobot
A leader in value-driven AI, DataRobot offers a collaborative approach to AI that combines its open AI platform and expertise to improve how customers run and optimise their businesses.
Its platforms are designed to simplify and automate the complex development of AI solutions, making it more accessible to those without coding knowledge.
DataRobot’s No-Code AI Apps allow the user to build and configure AI-powered applications using a no-code interface. As a result, they can enable core DataRobot services without having to build models, in addition to evaluating their performance in DataRobot.
8. Clarifai
Clarifai is an independent AI company first founded in 2013. It was one of the first deep learning platforms and specialises in computer vision, natural language processing and audio recognition. It provides an AI platform for unstructured image, video, text and audio data.
The Clarifai platform covers the entire AI lifecycle, which includes data preparation, model development, testing and evaluation. It also helps to get AI models or applications running smoothly without the need for code or machine learning operations (MLOps).
7. RunwayML
Founded in 2018, RunwayML’s mission is to build multimodal AI systems that will inspire the next generation of human creativity. It is a platform for artists to use machine learning tools in intuitive ways without needing coding experience.
With RunwayML, users can easily train and deploy AI models without the need for extensive coding knowledge. The platform supports a wide range of AI applications, including image synthesis, style transfer, natural language processing and object detection.
Users are able to upload their data or choose from a library of pre-existing models to generate AI-driven outputs.
6. Polymer
Polymer is a data security platform that uses advanced machine learning techniques to inspect data. The platform contextualises data so that users can easily identify threats and mitigate the risks of AI in real time.
Its intuitive business intelligence tool empowers users to create data visualisations, comprehensive dashboards and embed data into presentations without the need to write any code. Likewise, PolyAI, one of its features, is a built-in conversational AI assistant that analyses data and instantly generates visualisations based on instructions.
5. Apple CreateML
Apple CreateML is a machine learning framework and tool developed by tech giant Apple. It is specifically designed for macOS and iOS platforms and is designed to enable developers to build and train custom machine learning models.
The CreateML platform has a user-friendly interface that aims to simplify the process of training and deploying machine learning models across Apple devices.
With the platform, AI developers can create and deploy machine learning models that are tailored to their specific needs. In doing this, they can take full advantage of Apple’s hardware and software capabilities.
4. Google AutoML
Google AutoML is a suite of machine learning tools and services provided by Google Cloud that aims to simplify the process of building and deploying custom machine learning models. The platform offers a range of products and features that cater to different aspects of machine learning workflows.
Notably, AutoML also enables developers with limited machine learning expertise to train high-quality models specific to their business needs, as code is not required. Users and developers can build their own custom machine learning models within minutes.
3. Akkio
Founded in 2019, Akkio is a technology company with a mission to make AI simple enough for anyone to use, regardless of ability. Its no-code machine learning platform is designed to help modern sales, marketing and finance teams create and deploy AI predictive models.
Akkio combines machine learning technology with a streamlined, intuitive cloud platform to help businesses embrace the full potential of AI without the need for complex data or coding capabilities.
It is also one of the only AI data platforms specifically built for agencies to improve performance across entire client engagement lifecycles.
2. ObviouslyAI
ObviouslyAI has built a tool that enables non-technical business analysts to rapidly run predictions on their historical data. This is designed to enable businesses to make decision-making faster.
With ObviouslyAI, users can easily connect their data sources and utilise the platform’s automated machine learning capabilities to train and deploy predictive models. The platform is then able to automate the entire model-building process, including data preprocessing, feature selection, algorithm selection and hyperparameter tuning.
As a result, users can quickly generate accurate predictions without any need for manual intervention.
1. Amazon SageMaker
First launched in 2017, Amazon SageMaker is a cloud machine learning platform that is designed to enable AI developers to create, train and deploy machine-learning models in the cloud. It also enables developers to deploy machine learning models on embedded systems and edge devices.
SageMaker is built on Amazon’s two decades of experience developing real-world AI and machine learning applications, including product recommendations, personalisation, intelligent shopping, robotics and voice-assisted devices.
The platform aims to simplify and accelerate the process of building and deploying machine learning models. As a result, AI is more accessible to developers and data scientists who may not have specific expertise in managing the technology.
