Unite.AI

SEPTEMBER 30, 2024

Emerging Trends in AI Software Quality Control AI is reshaping how QA teams operate, from speeding up test creation to enhancing test data management. Here are a few emerging trends in AI software quality control: AI-powered Test Automation Creating test cases is now faster and more accurate with AI.

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The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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Marktechpost

JULY 17, 2023

The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.

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Unite.AI

MARCH 21, 2024

After that, I worked for startups for a few years and then spent a decade at Palo Alto Networks, eventually becoming a VP responsible for development, QA, DevOps, and data science. Could you discuss Amplitude’s core AI philosophy that AI should aid humans in improving their work rather than replacing them?

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Unite.AI

NOVEMBER 21, 2024

Archana Joshi brings over 24 years of experience in the IT services industry, with expertise in AI (including generative AI), Agile and DevOps methodologies, and green software initiatives. How does LTIMindtree’s AI platform address concerns around AI ethics, security, and sustainability?

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