Open||Source||Data How We Should Think About Data Reliability for Our LLMs with Mona Rakibe
This episode features an interview with Mona Rakibe, CEO and Co-founder of Telmai, an AI-based data observability platform built for open architecture. Mona is a veteran in the data infrastructure space and has held engineering and product leadership positions that drove product innovation and growth strategies for startups and enterprises. She has served companies like Reltio, EMC, Oracle, and BEA where AI-driven solutions have played a pivotal role.
In this episode, Sam sits down with Mona to discuss the application of LLMs, cleaning up data pipelines, and how we should think about data reliability.
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“When this push of large language model generative AI came in, the discussions shifted a little bit. People are more keen on, ‘How do I control the noise level in my data, in-stream, so that my model training is proper or is not very expensive, we have better precision?’ We had to shift a little bit that, ‘Can we separate this data in-stream for our users?’ Like good data, suspicious data, so they train it on little bit pre-processed data and they can optimize their costs. There's a lot that has changed from even people, their education level, but use cases also just within the last three years. Can we, as a tool, let users have some control and what they define as quality data reliability, and then monitor on those metrics was some of the things that we have done. That's how we think of data reliability. Full pipeline from ingestion to consumption, ability to have some human’s input in the system.” – Mona Rakibe
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Episode Timestamps:
(01:04): The journey of Telmai
(05:30): How we should think about data reliability, quality, and observability
(13:37): What open source data means to Mona
(15:34): How Mona guides people on cleaning up their data pipelines
(26:08): LLMs in real life
(30:37): A question Mona wishes to be asked
(33:22): Mona’s advice for the audience
(36:02): Backstage takeaways with executive producer, Audra Montenegro
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