Question: Can you recommend a data labeling platform that integrates with cloud storage and supports multiple projects and users?

If you need a data labeling platform that works with cloud storage and supports multiple projects and users, Label Studio is a great choice. It can handle a variety of data types, including images, audio, text, time series and video, and offers customizable layouts and templates. It also works with cloud storage systems like S3 and GCP and supports multiple projects and users, making it a good option for data scientists and companies of all sizes.

Another good option is SuperAnnotate, an end-to-end enterprise platform for training, evaluating and deploying AI models. It can pull data from local and cloud storage systems and offers customizable interfaces for different GenAI tasks. The platform includes sophisticated AI, QA and project management tools, supports a broad range of data types, and has a global marketplace of vetted annotation teams, so you can be sure your data sets are good and your models deploy easily.

If you want a full-stack data development platform, Encord is worth a look. It offers tools for ingesting data, cleaning it up, curating it, auto-labeling it and evaluating model performance, all of which is important for building computer vision applications that can make predictions and generate new imagery. Encord can integrate with a range of storage and MLOps tools and has data security protections like SOC2, HIPAA and GDPR compliance, so it's a good choice for companies that want to build their own AI.

Appen also offers a broad platform for collecting, curating and fine-tuning data. Its platform can handle a range of data types, and offers customizable workflows and built-in quality control mechanisms. With flexible deployment options and a focus on security and governance, Appen is a trusted partner for major brands and is a good choice for research and technology companies that want to advance AI data.