Quick references

This guide provides a structured overview of key functionalities and step-by-step instructions to help you efficiently navigate and utilize the Dataloop platform.

🚀 Quick Start References

• Sign In – Learn how to create an account, log in, and reset your password.
• Create or Join an Organization – Set up your workspace by creating a new organization or joining an existing one.
• Add Members to an Organization – Invite team members for seamless collaboration.
• Create a Project – Manage datasets and tasks within a dedicated project.
• Upload or Sync Your External Data – Bring in your data via uploads or cloud storage integration.
• Manage Your Datasets – Organize datasets, set up cloud storage drivers, and integrate cloud services.
• Set Up Ontology (Labels & Attributes) – Define labels and attributes for annotation tasks.
• Create a Labeling Task – Assign annotation tasks using Dataloop’s Annotation Studios.
• Use Labeling Studios – Perform annotations on images, videos, text, and PDFs.
• Create a QA/QC Task – Ensure high-quality labeled data with Quality Assurance and Control tasks.
• Install & Use AI Models – Integrate ML models and automate workflows.
• Create a Pipeline – Automate processes by connecting annotation, QA, and ML services.
• Create an Active Learning Pipeline – Use model predictions and confidence scores to prioritize uncertain data for annotation, improving ML model performance iteratively.
• Deploy & Install Application Services – Enhance your project with custom or marketplace services.

📂 Managing Datasets

• Creating & Importing Datasets – Set up and structure data efficiently.
• Dataset Organization – Use collections, metadata, and ML subsets for better management.
• Filtering & Searching – Locate relevant data quickly with advanced search tools.

🖍 Annotation & Labeling

• Labeling Studio – Annotate images, audios, videos, LiDAR, GIS, text, RLHF, and PDFs.
• Annotation Automation – Leverage AI-assisted labeling and automation tools.
• Quality Assurance – Validate annotations to maintain high-quality labeled data.

ML & AI Integration

• ML Subsets & Data Splitting – Organize datasets into train, validation, and test subsets.
• Model Deployment – Manage and deploy AI models within Dataloop.

🔗 API & SDK References

• SDK Documentation – Programmatically interact with Dataloop.
• API Guide – Integrate Dataloop with external applications.

🔒 User Management & Permissions

• Access Control - Project & Organization Level – Manage roles and permissions for teams.

📖 Additional Support

Release Notes – Stay updated on the latest features and improvements.
FAQs – Find answers to common questions.
Support & Contact – Get help from Dataloop’s support team.