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Data labeling - Best practices for project management & collaboration

Transforming raw unstructured data into high-quality training data is critical to delivering successful AI

Poorly labeled or noisy input data will lead to inaccurate output predictions regardless of how technically sound the predictive model is.
A mislabeled training sample or an imbalanced dataset can lead to an overestimation of an algorithm’s performance, only to discover that the labeled data isn’t representative of real-world patterns.

Data labeling doesn’t have to be a misery
Although necessary, the labeling process of raw unstructured is often perceived as a painful experience.
Discover how organizations have successfully optimized their data labeling with best practices on project management and collaboration

By the end of this step-by-step guide, you will be familiar with the best practices companies can emulate.

About Kili

 85% of AI projects never reach deployment.

At Kili Technology, we believe the foundation of better AI is good data.

Kili Technology's complete training data platform empowers large organizations such as IBM, Airbus and Capgemini to transform unstructured data into high-quality data to train their AI and deliver successful AI projects. This approach improves their teams’ productivity, accelerates the go-to-production cycles of their AI projects and delivers trustworthy AI.

Kili Technology is unique in its capacity to industrialize the collaborative labeling of unstructured data, enrich the process with Human-in-the-Loop intelligence and secure sensitive data.

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They trust us

IBM (2)

On leading the data-centric revolution: "Great companies like Kili Technology, (...) have already adopted this data-centric AI approach".

Andrew Ng
AI Thought Leader