Abha Laddha

Supercharge Your Data Quality: Automated QA

When it comes to AI, your model is only as good as the data it’s trained on. While it’s key to have a human in the loop when creating and verifying training data, automating processes within the workflow improves efficiency while guaranteeing high quality. Sama’s Auto QA functionality assesses tasks using a fixed set of rules configured to check for invalid combinations of labels and metadata. These checks are triggered before a task is submitted on our training data platform, allowing for the detection of errors early in the pipeline to save time and increase quality.

We discuss:

  1. Introduction to Sama Automated QA
  2. Eliminating 100% of logical fallacies with auto QA
  3. Increase data trainer efficiency by shortening the feedback loop
  4. Applications in automotive and hi-tech use cases

With a background in Computer Science, Abha leads the Customer Success Engineering team at Sama. The team is responsible for managing technical relationships with customers and prospects to understand their business needs, ideate upon them, and manage the implementation and communication of the solutions developed. Within the company, she wears many hats: from building complex solutions to deliver high quality data for complex workflows, to improving internal processes, and troubleshooting the in-house SamaHub platform. Her mission is to design and architect solutions that help clients achieve their larger ML vision while creating a positive social impact.

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