The fluctuations in big tech stocks highlight challenges faced by language model products, particularly their user traction. At ODSC Europe 2024, Noe Achache discussed these issues and emphasized adherence to the traditional NLP development lifecycle: Plan, Prepare Data, Engineer Model, Evaluate, Deploy, Operate, and Monitor. Although LLMs bring advancements, they also complicate processes, underscoring the need for robust data preparation, comprehensive evaluation, and meticulous monitoring of model behavior to ensure product reliability and improve user engagement.
Despite the revolutionary capabilities of LLMs, the core development lifecycle established by traditional natural language processing remains essential.
Poorly prepared data leads to subpar evaluation and iterations, reducing generalizability and stakeholder confidence.
Without rigorous evaluation, developers face pointless iterations, limited insights into model behavior, and challenges in establishing product reliability.
Neglecting to monitor user interactions and data drifts hampers insights into product adoption and long-term performance.
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