Introduction
To improve H2O.ai's Driverless AI product and streamline the feature engineering process, we need to conduct a comprehensive analysis of the current product, user needs, and market trends. I'll approach this challenge by examining key stakeholders, identifying pain points, generating innovative solutions, and proposing metrics to measure success.
Step 1
Clarifying Questions (5 mins)
Why it matters: Determines the complexity and depth of features we should focus on Expected answer: Mix of citizen data scientists and experienced ML practitioners Impact on approach: Would balance ease-of-use with advanced customization options
Why it matters: Helps prioritize which aspects of feature engineering to improve Expected answer: Time-consuming process, lack of explainability, difficulty in handling specific data types Impact on approach: Would focus on automation, interpretability, and data type support
Why it matters: Identifies areas where we can further strengthen our unique value proposition Expected answer: Strong in time-series data, but lagging in NLP feature engineering Impact on approach: Would prioritize improvements in NLP feature engineering while maintaining our lead in time-series
Why it matters: Influences whether we focus on new feature development or optimization of existing ones Expected answer: Mature product looking to expand market share Impact on approach: Would balance between refining core features and adding new capabilities to attract a wider user base
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