DATA STRATEGY
DATA STRATEGY
Strategies to
unlock quality data annotation at scale
We help you navigate the growing complexity of data preparation to train smarter, high performance AI. Relying on our deep experience in the annotation space, we evaluate your project needs and current capabilities and recommend the tools, teams and processes needed to deliver excellent results — at scale.
Project and workflow design
Starting with a thorough project analysis, we work with you to find potential pitfalls in the data preparation process and create a project design to greatly increase the resulting data quality and efficiency in your workflows from the outset.
- Optimize tasks and steps in data preparation pipeline for maximum efficiency
- Unlock scalability for massive data annotation projects
- Discover where automation or ML tools can increase annotation speed and quality
Annotation strategy and quality assessment
Guideline definition and refinement
Annotator team curation
Quality assessment
Our 5 factors of data quality
Volume
Is the dataset large enough to adequately train the algorithm?
Coverage
Does the dataset cover all necessary conditions?
Balance
Are all cases covered in equal proportions?
Accuracy
Are annotators labeling the data accurately?
Consistency
Do different annotations stay consistent to the guidelines or is there ambiguity?
Tooling strategy
When it comes to annotation tools, one size doesn’t fit all — even small adaptations can prevent errors and win you seconds of annotation time that can add up tens of thousands of saved hours. We evaluate your project and make a recommendation for the optimal combination of tools, with a focus on business value.
28,835 hours of work saved
By adapting the annotation tool in an annotation project covering 10,000 hours of spontaneous speech, we saved 2.9 hours of work per audio hour, resulting in a saving of 28,835 hours of work — the equivalent of 15 annotators working full time for a year.
Security & privacy
As AI applications broaden in scope, security and privacy take on new significance. We have the most rigorous security and privacy procedures in the industry, and offer guidance on the levels needed for your use case, industry and compliance needs.
2015: Setup of our first secure annotation facility
For sensitive projects, we provide end-to-end support in designing and implementing security and privacy procedures, up to and including creation of secure annotation facilities.
Ethical AI
Building good AI means considering its impact on people from the start. We help you identify where bias can creep into training data — from imbalanced datasets to process and team decisions — and give you recommendations on how to improve.
Recommended content
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.
The machine learning workflow
The key components of any machine learning workflow are data collection, model training, and testing, and model error analysis. How to implement each phase depends on unique project needs.
Minimizing the risks of outsourced data annotation
As the world becomes more AI-driven, the confidentiality of machine learning data is paramount. Choosing a company to trust with your sensitive data can seem daunting – but it doesn’t have to be. Sigma.ai approaches data annotation with a security-first mindset, setting us apart from our competitors.
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.