Every project is a learning experience. Data science is based on "Data". Working with no data, less data, or encrypted domain knowledge with minimal data has been challenge over the past 4 years. Yet, even when data is plentiful, there remains a balancing act between leveraging it effectively and mitigating trust issues, as collaboration can sometimes be overshadowed by the scramble for credit. Everyone wants to work on a model, not on data, the old google paper still comes into their eyes :). The current trend is to train large language models (LLMs) on uniform datasets, yet this approach glosses over an important truth: no dataset can capture the full spectrum of reality. Issues such as digital poverty, underrepresentation, and inherent biases are embedded within the data we collect. Without addressing these challenges, solutions can be superficial and short-lived. Moving fast with a lot of guardrails is essentially a band-aid, not a solution. Take a step back and balance data vs model. Build something that lasts forever not for paychecks!!!
Keep Thinking!!!
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