Mental models for ML products

Machine learning (ML) models are increasingly influential in various sectors, offering potential transformations in decision-making and human-computer interactions. To navigate the overwhelming landscape of ML products, mental models can be employed to simplify complexities, as outlined in the full-stack deep learning course from UC Berkeley. These models categorize ML products into three archetypes: Software 2.0, Human-in-the-loop (HIL) systems, and Autonomous systems. Software 2.0 leverages ML to replace or enhance rule-based systems, with the concept of data flywheels improving model performance through user engagement and data collection. HIL systems incorporate human oversight to refine ML outputs and enhance user experience, often utilizing user feedback to enrich datasets. Autonomous systems, exemplified by self-driving cars, function independently without human intervention, yet incorporating a human in the loop can mitigate risks and aid in dataset development. Understanding these archetypes aids in analyzing ML projects and determining the best fit for a particular product.