我使用探索性数据分析（EDA）和本地大语言模型（LLMs）来做出更好的产品决策。
产品经理如何通过更智能的EDA提升AI/ML产品决策能力
好的决策始于好的问题。在产品经理决定构建哪些功能之前，一个被忽视但至关重要的步骤是：探索性数据分析（EDA）。
为什么？因为良好的EDA能够加速假设验证，帮助你提出正确的问题，测试假设，应用统计技术，揭示用户旅程的洞察，明确关键绩效指标（KPI）——最终帮助你做出更聪明、更具战略性和以用户为中心的决策。
多年来，我开发了一种轻量但有效的流程，帮助我和团队更快地推进——尤其是在以下情况下：
- 处理不能离开本地网络的敏感或个人身份信息（PII）数据
- 处理非常大的数据集
- 精简与分析师和数据科学家的协作
以下是我常用的设置，节省了我们数天（如果不是数周）的时间：
- 数据分析与可视化 — Jupyter Notebook + Pygwalker
不再需要导出CSV文件或在商业智能工具与原始数据之间来回切换。
- 本地大型语言模型（LLM）与LMStudio
当我需要帮助探索假设、撰写SQL或总结发现时。
这是我关于这个主题的LinkedIn帖子：
https://www.linkedin.com/posts/peekay-chan-453102_pygwalker-lmstudio-productmanagement-activity-7352780192395792384-AFDe?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAD2_8BA9f2IpYDPZmflw9ziUIVH_mw7V8
希望听听其他产品经理、分析师和数据科学家是如何处理这个问题的。
查看原文
How product managers supercharge AI/ML product decision-making with smarter EDA.
Good decisions start with good questions. Before product managers decide what features to build, one of the most overlooked yet critical steps is: Exploratory Data Analysis (EDA)<p>Why? Because good EDA accelerates hypothesis validation, helps you ask the right questions, test assumptions, apply statistical techniques, uncover insights about the user journey, clarify KPIs — and ultimately helps you make smarter, more strategic, and user-centric decisions.<p>Over the years, I’ve developed a lightweight but effective process to help myself and teams move faster — especially when:
dealing with sensitive or PII data that can’t leave local networks
working with very large datasets
streamlining collaboration with analysts and data scientists<p>Here’s my go-to setup that has saved us days (if not weeks):
Data analysis & visualization — Jupyter Notebook + Pygwalker
No more exporting CSVs or bouncing between BI tools & raw data.
Local LLM with LMStudio
When I need help exploring hypotheses, drafting SQL, or summarizing findings<p>Here is my LinkedIn post about this topic.
https://www.linkedin.com/posts/peekay-chan-453102_pygwalker-lmstudio-productmanagement-activity-7352780192395792384-AFDe?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAD2_8BA9f2IpYDPZmflw9ziUIVH_mw7V8<p>Would love to hear how other PMs/Analysts/Data Scientist go about this.