AI Training Data Curation & Collection

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are vital in orchestrating the curation and collection of AI training data.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to build datasets that power AI systems.

Training and Onboarding

PMs design and implement training programs to ensure workers understand data standards, project goals, and quality benchmarks. For example, in rare language collection, PMs might train annotators on dialect nuances, using sample recordings and guidelines. Onboarding includes hands-on data processing, feedback sessions, and calibration exercises to align worker outputs with AI requirements. PMs also establish workflows, such as tiered reviews for complex multimodal datasets.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., collecting 20,000 audio samples) and set metrics like completeness, accuracy, or diversity. They track progress via dashboards, resolve bottlenecks, and refine processes based on worker feedback or client needs.

Collaboration with AI Teams

PMs connect data curators with machine learning engineers, translating technical needs (e.g., balanced class distributions) into actionable collection tasks. They also manage timelines to sync data delivery with AI training cycles.

We Manage the Tasks Performed by Workers

The collectors, curators, or annotators perform the detailed work of building and refining AI training datasets. Their efforts are meticulous and context-aware, requiring precision and adaptability.

Common tasks include:

Labeling and Tagging

For bias analysis, we might tag dataset entries as “underrepresented” or “overrepresented.” In speech-text alignment, they label audio segments with corresponding text.

Contextual Analysis

For custom data collection, our team evaluates sources for relevance, ensuring alignment with project goals. In dataset balancing, they assess sample distributions for fairness.

Flagging Violations

In data cleaning, our employees and subcontractors flag inconsistencies (e.g., garbled audio, corrupt files), ensuring only usable data proceeds to training.

Edge Case Resolution

We handle complex cases—like ambiguous dialects or multimodal mismatches—often requiring discussion or escalation to specialists.

We can quickly adapt to and operate within our clients’ data platforms, such as proprietary curation tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of items per shift, depending on task complexity.

Data Volumes Needed to Improve AI

The volume of curated data required to train and enhance AI systems is substantial, driven by the need for diversity and quality.

While specifics vary by task and model, general benchmarks include:

Baseline Training

A solid model might require 10,000–50,000 curated samples per category (e.g., 50,000 cleaned text entries). For multimodal tasks, this could rise to 100,000 to cover all formats.

Iterative Refinement

To enhance performance (e.g., reducing bias from 10% to 2%), an additional 5,000–20,000 samples per issue (e.g., underrepresented dialects) are often needed. For instance, balancing a dataset might demand 15,000 new entries.

Scale for Robustness

Large-scale AI (e.g., global speech recognition) requires datasets in the millions to address rare cases, languages, or modalities. A synthetic data project might start with 100,000 generated samples, expanding by 50,000 annually.

Active Learning

Advanced systems use active learning, where AI flags gaps for curation. This reduces volume but demands ongoing effort—perhaps 1,000–5,000 new samples weekly—to maintain quality.

The scale necessitates distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and efficiency.

Multilingual & Multicultural AI Training Data Curation

We can assist you with your AI training data curation and collection needs across diverse linguistic and cultural landscapes.

Our team is equipped to source and refine data for global applications, ensuring comprehensive and culturally relevant datasets tailored to your objectives.

We work in the following languages: