Best 8 ai assisted data labeling Tools in 2024

Flexible data labeling for all data types
Support for computer vision, natural language processing, speech, voice, and video models
Customizable tags and labeling templates
Integration with ML/AI pipelines via webhooks, Python SDK, and API
ML-assisted labeling with backend integration
Connectivity to cloud object storage (S3 and GCP)
Advanced data management with the Data Manager
Support for multiple projects and users
Trusted by a large community of Data Scientists

To use Label Studio, you can follow these steps: 1. Install the Label Studio package through pip, brew, or clone the repository from GitHub. 2. Launch Label Studio using the installed package or Docker. 3. Import your data into Label Studio. 4. Choose the data type (images, audio, text, time series, multi-domain, or video) and select the specific labeling task (e.g., image classification, object detection, audio transcription). 5. Start labeling your data using customizable tags and templates. 6. Connect to your ML/AI pipeline and use webhooks, Python SDK, or API for authentication, project management, and model predictions. 7. Explore and manage your dataset in the Data Manager with advanced filters. 8. Support multiple projects, use cases, and users within the Label Studio platform.

Data annotation services for various industries
AI-powered labeling platform (BasicAI Cloud)
Auto-annotation and object tracking capabilities
Real-time and batch quality assurance
Scalable labels management
Collaboration and team management features

To use BasicAI, you can leverage their data annotation services or utilize their AI-powered data labeling platform, called BasicAI Cloud. The platform offers features like auto-annotation, object tracking, and scalable labels management. You can collaborate with your team, manage workflows, and ensure quality assurance using BasicAI Cloud.

Global data labeling platform
Elite workforce in 40+ languages
Integration with modern APIs and tools

To use Surge AI, simply sign in to the website and access the platform. From there, you can create labeling projects, set labeling instructions, and manage the labeling workforce.

Data Labeling for Computer Vision and NLP models
Experienced team of annotators
Ethical outsourcing practices
Proximity management
Competitive rates
Data security and confidentiality
High-quality labeled data

Contact us to outsource your data annotation tasks for AI models

AI-powered text analysis and labeling
Web research agents that can crawl the web and fill out research tables
Content quality analysis to rate and rank content and survey responses
Seamless integration with Google Sheets and Excel
Custom AI models for specific data needs
Fast and reliable performance
Instant intelligence on rows of data
Repeatable workflows for enhanced efficiency
Ability to customize and adapt models to work processes

To use PromptLoop, simply install the plug-in and integrate it into your spreadsheet software. You can then access the AI models directly within your spreadsheets to perform tasks such as intelligent tagging, labeling, analysis, web research, and content quality analysis. It also allows you to train and utilize custom AI models specific to your data needs. PromptLoop offers a user-friendly interface that makes it easy for anyone to extract valuable insights from complex information.

Expert labelers
Advanced labeling tools
Meticulous methodology
Ability to handle complex projects
Collaboration and communication with clients

To use People for AI's data labeling services, you need to contact them through their website or by emailing them. They will assign you a project manager who will work with you to understand your project requirements and define the data labeling strategy. Once the strategy is finalized, their expert labelers will start labeling your dataset using their specialized tools. Throughout the project, they provide regular communication and progress updates to ensure your satisfaction with the results.

1. Data curation: Curate valuable unlabeled data to maximize model improvement. 2. Metadata registration: Register metadata to keep your data secure and accessible. 3. Diagnostics: Use a data-centric toolkit to identify model failure modes and regressions. 4. Active learning miners: Sample the most valuable unlabeled data with these miners. 5. Labeling and retraining integration: Integrate Dioptra with your labeling and retraining stack.

1. Curate the most valuable unlabeled data to improve domain coverage and model performance. 2. Register your metadata to Dioptra to ensure your data remains with you. 3. Diagnose root cause model failure modes and regressions using Dioptra's data centric toolkit. 4. Use active learning miners to sample the most valuable unlabeled data. 5. Integrate with your labeling and retraining stack using Dioptra's APIs.

AI-driven insights
Interactive, customizable dashboards
Data control and security
Cloud integration
Simple and intuitive interface

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