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MLOps.community
Cleanlab: Labeled Datasets that Correct Themselves Automatically // Curtis Northcutt // MLOps Coffee Sessions #105
Jul 1, 2022
01:06:10
Snipd AI
In this episode, Curtis Northcutt, CEO & Co-Founder of Cleanlab, discusses the importance of data-centric AI and the challenges of addressing noisy data. They also delve into the journey of Cleanlab in improving data labeling accuracy, the success of the startup in finding and correcting bad data, and the frustrations of bug smashing. Additionally, they explore the challenges of understanding the value and capabilities of AI tools and companies, as well as the hiring opportunities in DevRel and front-end engineering.
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Quick takeaways
- Clean Lab is an open-source/SaaS company building data-centric AI tools for automatically correcting messy data and labels.
- Clean Lab's tool can identify label errors in machine learning models and improve data quality.
Deep dives
The Power of Data-Centric AI and MLOps
Data-centric AI and MLOps are two important concepts in the field of machine learning operations. While MLOps focuses on ML operations and can involve the model and infrastructure, data-centric AI is more academic and industry-oriented. One major difference between the two is that data-centric AI emphasizes the importance of improving data quality to enhance the AI pipeline. In contrast, MLOps encompasses a wider range of operations and infrastructure improvements. An example of the significance of data-centric AI is its effectiveness in improving machine learning with noisy data and labels, where methods that alter the data outperform methods that alter the model. This highlights the need to focus on improving data quality in certain machine learning problems.
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