我用 EDA 和本地 LLM 做出更好的产品决策
2 分•作者: pdxbug•7 个月前
产品经理如何通过更智能的 EDA 助力 AI/ML 产品决策
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，一个最容易被忽视但至关重要的步骤是：探索性数据分析 (EDA)。
为什么？因为好的 EDA 可以加速假设验证，帮助你提出正确的问题，测试假设，应用统计技术，揭示关于用户旅程的见解，明确 KPI——最终帮助你做出更智能、更具战略性和以用户为中心的决策。
多年来，我开发了一个轻量但有效的方法，帮助自己和团队更快地行动——尤其是在以下情况下：
* 处理无法离开本地网络的敏感或 PII 数据
* 处理非常大的数据集
* 简化与分析师和数据科学家的协作
以下是我的常用设置，为我们节省了数天（甚至数周）的时间：
* 数据分析和可视化——Jupyter Notebook + Pygwalker
不再需要导出 CSV 文件或在 BI 工具和原始数据之间切换。
* 带有 LMStudio 的本地 LLM
当需要帮助探索假设、起草 SQL 或总结发现时
这是我关于这个话题的 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](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)
很想听听其他 PM/分析师/数据科学家是如何做的。
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.