The author reflects on their experience with research papers and fine-tuning, identifying data quality as the most significant challenge in the process of training machine learning models. Key issues include removing noise, ensuring diversity and distribution, and acquiring sufficient data volume. Initially, they thought the main challenge would be in design and programming; however, they find that accessible research and tools, like Claude for prototyping, have mitigated those concerns. The author wonders how long it will take for individuals to train their own foundational supervised machine learning models, provided they have the necessary hardware and dedication. They invite readers to share their thoughts on the biggest challenges they face in this context.
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