Build with AI and Data Partners
In my early days as a Product Manager, PMs main build partners were primarily developers. Over time, this group has expanded to include roles like data scientists, data engineers, product analysts, ML engineers, knowledge engineers, and conversational designers, among others.
In this presentation, I'll provide an overview of different types of AI products a mental model for partnering with these diverse roles throughout the product lifecycle. We'll cover various AI products, including:
- Predictive ML products (e.g., applicant evaluation, targeting)
- ML Platforms: which provide infrastructure for hosting and operationalizing hundreds of ML models
- Generative AI products (e.g., content summarization)
- Semantic Search
We'll then delve into two of the four products, examining each partner's contributions during development and outlining best practices for PM interactions.
A special emphasis will be placed on data engineers and analysts. Despite being often overlooked, their roles are crucial in sourcing data, building pipelines, and generating insights through techniques like A/B testing and cohort analysis, all of which are foundational for creating effective AI products.