Top 13 Training Data Platforms

Data is an essential part of the quality of machine learning models. Supervised AI/ML models require high-quality data to make accurate predictions. Training data platforms streamline data preparation from collection to annotation, ensuring high-quality inputs for AI systems.

See the top training data platforms, split by data marketplaces and data labeling tools, and mapped to their core data functions:

• Commercial data providers/marketplaces
• Open-source data hubs
• Data labeling tools
  

Data marketplaces

Commercial data providers

These supply curated datasets and ready-to-use datasets for purchase.  To learn more, check out data annotation services.

• IBM Data Asset eXchange (DAX): Offers high-quality datasets with open licenses, integrated with IBM Cloud and Watson, providing supplementary resources.  
• Microsoft Azure Open Datasets: Provides curated public datasets optimized for machine learning workflows, integrating with Azure AI and ML tools.
• AWS Data Exchange: A commercial data marketplace offering access to over 3,500 third-party datasets (medical, satellite, financial), including free and open data products. It serves various industries like financial services, healthcare, and media, enabling seamless discovery and subscription to data for cloud-native ML pipelines.
• Snowflake Data Marketplace: Serves as a conduit linking data providers with consumers, integrating seamlessly with Snowflake’s data cloud for live data access and secure data sharing.

Open-source data hubs

Communal repositories offering public/shared datasets.

• Hugging Face Hub: An open-source platform and library for leveraging machine learning models, hosting thousands of pre-trained models (~900k) and ready-to-use datasets (~90k). It simplifies AI integration for tasks like conversational AI, natural language processing (NLP), and computer vision (CV), offering integrated preprocessing and fine-tuning capabilities.
• Roboflow Universe: A community-driven open-source data hub, providing a repository of over 100,000 open-source datasets primarily for computer vision applications. It supports dataset hosting, versioning, and offers integrated tools for data exploration, visualization, and AI-assisted auto-labeling.
• LAION: A non-profit open-source data hub dedicated to providing massive machine learning resources, including colossal image-text datasets like LAION-5B (5.85 billion pairs). It powers open computer vision (CV) training data and supports multimodal AI research, including audio and video understanding.
• Kaggle Datasets: A widely used platform hosting a collection of public datasets, often for competitions.

Data labeling tools

Focused on annotation workflows, often with model-assisted tools, for creating training datasets. To learn more about data labeling tools.

• Labelbox: Offers an AI platform for generating high-quality, industry-specific training data. It provides interactive workflows, AI-powered annotation tools for automatic suggestions and batch processing, as well as quality control for various data types, including images, text, video, audio, and multimodal data.
• Dataloop: An AI-powered data annotation platform that supports building production-grade unstructured and semi-structured data pipelines. It offers comprehensive data management, collaborative labeling, auto-suggestions, and seamless integration of human feedback.
• Sama: Provides powerful human-in-the-loop data annotation solutions, leveraging a workforce and an ML-powered platform. It delivers quality annotations for image, video, and 3D point cloud data.
• CVAT: Computer Vision Annotation Tool is a leading open-source platform for computer vision annotation. It offers a wide range of tools for images, videos, and 3D data, supporting tasks like object detection and segmentation. CVAT features automated labeling, significantly accelerating the annotation process.
• Label Studio: A flexible open-source data labeling platform for preparing training data, fine-tuning large language models (LLMs), and validating AI models. It supports a wide array of data types, including text, audio, images, video, time series, and multi-domain applications, offering configurable layouts and ML-assisted labeling.

What are training data platforms?

Training data platforms are software that automates the following processes for companies:

• Labels Data: Training supervised ML models requires processes such as image, text, and audio annotations. Training data platforms provide automated labeling for enterprises.
• Diagnostics: Training data platforms identify model errors and understand performance trends that help the IT team monitor models.
• Prioritize: It is not optimal for organizations to spend time on labeling poor-quality data. Training data platforms determine the most effective use of data.

Why are training data platforms important?

McKinsey¹ argues that data-related issues are the biggest struggle in developing effective ML models. In this regard, training data platforms that provide solutions for reaching high-quality data directly impact the competitiveness of companies.

These platforms solve critical bottlenecks:

• Eliminate labeling bottlenecks: Manual data labeling can be a time-consuming and labor-intensive process. Auto-annotation and AI-assisted labeling features cut processing time from weeks to hours.
• Ensure data diversity: Training data platforms facilitate access to diverse commercial and open-source datasets, solving representation gaps and preventing models from inheriting biases that could impact performance and fairness.
• Reduce costs: Inefficient data preparation leads to wasted resources. By prioritizing high-quality data and optimizing labeling workflows, these platforms help avoid wasted resources on unusable samples.

If you need help choosing the right vendor that will improve your data quality, contact us:

External Links

• 1. What AI can and can’t do (yet) for your business | McKinsey. McKinsey & Company