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"especially because the next challenge with the democratization of LLMs will demand differentiation at the data level, especially of higher quality data for fine-tuning models"

What is "demand differentiation" at the data level?

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What I meant by that was your ability to create truly remarkable & unique product experiences driven by ML is in collecting, aggregating & curating high quality data -- usually by thoughtful design and instrumentation of the application and services generating the data.

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Wonderful article ... thank you for sharing! Where are people getting high quality web data from? Scraping it themselves?

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Thanks for the kind words!

In terms of where people are getting data from for ML (whether training from scratch or fine-tuning), there's a number of acquisition mechanisms:

- Scraping

- Purchasing from data brokers

- Use existing sources in-house they haven't used before

- 1st party data generated by their application and services

In terms of quality, ia high-quality dataset for one use case might not be high-enough quality for another use case.

For example, a Reddit dataset consisting of all the posts & replies on the r/NFL + a scraped website of football stats could be really useful for a fun chatbot used for bar night trivia. However that same dataset would be useless for created an automated, real-time sports commentator for a Spanish channel.

So part of what feeds into quality is relevancy to the use case and the best way to create high-quality data is either leveraging existing data within a company or by working with the application developers to help engineer the first party data flywheel.

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Great post! Waiting for the next one to get into the measures 🤩

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Thank you! Draft for the next post was sent to Chad & Mark, so definitely on the horizon!

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