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.

Strategies to Unlock
Project Design and Workflow

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.

Annotation strategy and quality assessment

Guideline definition and refinement

Annotation guidelines — the rules annotators use to label data consistently — are a major factor in the resulting quality of the training data. We help you define precise, well-structured guidelines from the outset that serve your model, and refine them further during the annotation process.

Annotator team curation

Different projects call for annotators with specific skill sets and expertise. Starting with a project manager who deeply understands your use case, we recommend a team of annotators that will deliver you the best quality results.

Quality assessment

Quality is our first thought, not an afterthought. We start by proactively assessing the database itself, then create continuous feedback loops during the annotation process. Once the model is tested, we can reactively evaluate if any steps in the data preparation process led to errors, and make adaptations as necessary.

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.

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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.

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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.

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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.