TOP 10 ‘NO-CODE’ MACHINE LEARNING PLATFORMS TO KNOW IN 2022
Here is a list of the top 10 no-code machine learning platforms to look into in 2022
No-code decouples programming languages and syntax from logic and instead takes a visual approach to software development to enable rapid delivery. No-code ML is a subset that tries to make ML more accessible. To deploy artificial intelligence and machine learning models, no-code ML involves adopting a no-code development platform with a visual, code-free, and frequently drag-and-drop interface.
Machine learning doesn’t have to be reserved for technical programmers. So, with no-code ML platforms, analysts have the power of data predictions to help them move faster, which means they can help their businesses to think creatively and proactively. The no-code application platforms have shown a lot of promise and productivity gains. Such platforms help organizations to automate and digitize processes with cloud-based mobile apps. If you need to quickly deploy a machine learning component and integrate it with your existing program, here is a list of no-code platforms to look into.
CreateML: This is a no-code drag and drop platform available for iOS developers by Apple to create and train custom machine learning models on Mac. Today CreateML is an independent macOS application that comes with a bunch of pre-trained model templates.
Fritz AI: It is a growing machine learning platform that helps bridge the gap between mobile developers and data scientists. It gives you flexibility in how much you want to be invested in ML model development and can train custom models in the Studio or use pre-trained models.
Google AutoML: This no-code platform enables developers with limited machine learning expertise to train high-quality models specific to their business needs. And allows developers with limited ML experience to train models specific to their use cases. The platform works with different types of data and covers a broad range of use cases from computer vision and video intelligence to NLP and translation
MakeML: This platform is meant for creating object detection and segmentation neural networks. It provides a macOS app for iOS developers to create and manage datasets. Using this tool, you can outline and edit elements not only in photos but also in videos.
RunwayML: This platform is to make ML techniques accessible to students, and creative practitioners from a wide range of disciplines. It provides a delightful visual interface to quickly train models ranging from text and image generation to motion capture.
Obviously AI: This platform uses state-of-the-art NLP to perform complex tasks on user-defined CSV data. It can be used by marketers and business owners who want to forecast revenue flow, optimize business processes, build a more effective supply chain, and conduct personalized automated marketing campaigns.
Data Robot: It is a popular end-to-end enterprise AI platform for the fast and easy deployment of accurate predictive models. It enables business analysts to build predictive analytics without knowledge of ML or programming. It aids in the preparation, development, deployment, monitoring, and maintenance of enterprise-scale AI applications.
BigML: It is an open-source platform that offers machine learning and application integration services to businesses. It offers business analysts and application integration with commoditized ML as a service. It can build a machine learning or deep learning model with just 3-4 clicks.
SuperAnnotate: It is an AI-powered annotation platform that uses machine learning capabilities to boost your data annotation process. It covers a multitude of cases across different industries such as aerial photography, autonomous driving, robotics, and medicine. It is an end-to-end platform to annotate, train and automate computer vision pipelines.
Teachable Machine: This platform uses the Tensorflow.js library in your browser and ensures that your training data stays on the device. It is a no-code platform to create machine learning models for your sites, apps, and more.
Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.
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