DATA ANNOTATION
DATA ANNOTATION
Human intuition, at scale
Much like a child, an AI needs examples to learn how to interpret information correctly. Human judgment is key to setting the right criteria and making the right calls when annotating training data. This is why Sigma puts humans, facilitated by our suite of customizable annotation tools and automated processes, at the heart of our annotation projects.
Data labeling
Criteria, defined by experts
Curated annotation teams
Data labeling tools
Data labeling automation tools
Automating manual aspects of the data labeling process greatly increases speed, accuracy and throughput. Sigma uses a suite of automation tools for data pre-and post-processing across speech, text, image and video to support human annotators in doing their best work
Machine-learning assisted data labeling
By tightly integrating machine learning models with the human annotators’ processes, we can create feedback loops where an algorithm uses already-annotated data to predict how a new piece of data could be labeled. Annotators receive data with a pre-filled suggestion, increasing consistency and speed.
In one image annotation project, we used a machine-learning model to help predict the shape of an object using polygons. The number of polygons annotated per hour increased by 60%.
Annotation for text, speech, image, video and more
Supported by our suite of machine learning and automation tools that cover a broad range of data types and annotation techniques, we combine the right teams, tools and processes to deliver you the highest quality training data.
Data annotation services
SPEECH AND TEXT
Transcription and diarization
Entity recognition
Intent recognition
Data relevance
Sentiment and emotional analysis
Pronunciation and dialect assessment
Conversational AI annotation
Translation and localization
Content moderation
IMAGE AND VIDEO
2D & 3D bounding boxes
Polygons
Lines and splines
Landmark annotation
Optical character recognition
Image classification
Semantic segmentation
Video tracking
Don’t see the type of annotation you need? Get in touch we thrive on unique and challenging projects!
Continuous quality feedback
Quality assurance works best when it’s integrated tightly into running annotation workflows. Throughout the process, project managers continuously review the annotators’ work and provide feedback to clarify any issues and reestablish consistency
Tech-supported, preventative QA
Our QA approach includes preventative, automated quality checks that are built directly into the annotators’ user interface and specialized to the type of data being labeled. This assures annotators follow every step in the process, limits inconsistencies and prevents errors before they happen.
Continuous optimization: tools and guidelines
During the continuous feedback loops, our project managers step in to make iterative optimizations that improve annotation quality immediately, during the running process. We customize tools, guidelines, and processes on a continuous basis to streamline annotation and increase efficiency.
Tools customization
We customize our suite of data labeling and annotation tools according to your unique workflow to reduce time spent in individual annotation steps and further improve labeling accuracy and consistency.
Guideline refinement
Our project managers continuously refine guideline definitions through feedback loops and consensus-building with annotators, making them more precise, clearer, and easier for annotators to follow.
Recommended content
What is data annotation?
Data annotation describes the categorization and labeling of data for AI applications. When data gets attributed, tagged, and labeled, it's easier for machine learning models to understand what the data is all about and retain relevant information.
Embedding data annotation into search algorithm development
When iterating on a running search algorithm, engineers turned to Sigma’s flexible annotation teams to evaluate queries and respond to changes on the fly.
Building a scalable data annotation strategy
Creating high-quality datasets is essential to successful artificial intelligence (AI) and machine learning (ML) projects. Outsourcing your data annotation strategy might be the best way to ensure your data annotation is done properly and remains flexible.
Let’s work together to build smarter AI
Whether you need help sourcing and annotating training data at scale, or you need a full-fledged annotation strategy to serve your AI training needs, we can help. Get in touch for more information or to set up your proof-of-concept.