Best practices to scale human data annotation for large datasets
The data dilemma: How much training data is enough for LLMs? Among the many challenges of training LLMs is the demand for gigantic amounts of training data. The exact volume varies based on the model’s intended use case and the complexities of the language domain. To determine the optimal dataset size, experts recommend experimenting with […]
How do you know it’s time to outsource data annotation?
You need to move quickly but without compromising quality. In-house annotation? For many organizations, it isn’t sustainable anymore. But how do you know it’s time to outsource data annotation? If you’re struggling to keep pace with your data annotation demands, facing a bottleneck, or simply want to optimize your AI development pipeline, read on to […]
What is data annotation? Types, challenges, and getting started
Data annotation involves tagging unstructured data to fuel machine-learning models. Learn how it works and how to get started with our guide.
Human data annotation: behind the scenes of quality Gen AI
Human data annotation is vital to training Gen AI. Learn how Sigma.AI achieves excellence through project management and expert teams.
¿Qué es el procesamiento del lenguaje natural?
Natural Language Processing uses software to analyze, understand, and manipulate human language. Learn how it can benefit your business.
Training data for machine learning: here’s how it works
Discover the crucial role of training data for machine learning. Learn how it’s used to teach algorithms to make predictions and decisions.
Establishing ground truth data for machine learning success
Accurate, real-world data improves AI performance. Discover the crucial role of ground truth data in machine learning and how to obtain it.
What is synthetic data? Types, challenges, and benefits
Learn about the different types of synthetic data, the hurdles in creating it, and how it’s revolutionizing industries.
Linguistic diversity in AI and ML: Why it’s important
La diversidad lingüística en la IA significa más acceso, inclusión y casos de uso para todos los que se benefician del aprendizaje automático.