By Joshua Golde
A Google-backed platform that allows developers with limited ML expertise to train high-quality models specific to their business needs. The platform focuses on image and video annotation and labeling. As well as semantic text analysis and classification.
An Apple-backed no-code platform that uses a Mac OS framework. It allows users to easily build ML models with an easy-to-use app interface and no code. The platform can train a variety of models, from image recognition to sentiment and regression analysis.
A no-code platform that allows users to train and build AI models. The platform focuses on image, text, and document classification. It enables users to train custom models on their use-case-specific data. Custom models also have a human-in-the-loop option, which means the model asks for input where it is unsure. Some use cases are automated data iteration, and classification of images, texts, and documents.
A no-code AI platform that builds ML algorithms for data prediction. Users can take a bird’s-eye view of existing data, understand it and draw conclusions. The platform also suggests ready-made datasets, so you can test them out and get predictions right away. Some business-case usage would be personalisation of marketing campaigns, forecasting of company revenue, and supply chain optimisation.
A leading platform designed to build the highest quality training datasets available for computer vision and natural language processing. This platform has advanced tools, like automation features, data curation, offline access, and integrated annotation services. It allows ML teams to build incredibly accurate datasets 3-5x faster.
