From complex business ideas to manageable ML components
"We can't directly process audio and come up with answers; there's an intermediary step."
— John, Data Scientist
Business ideas often start as monolithic AI solutions, but technical implementation requires decomposition into manageable components.
"Build a Siri for real estate: users speak to our app and receive curated property results like a human broker."
What seems like one AI product actually requires multiple specialized components:
Breaking down complex AI products into manageable components using a systematic approach.
Identify raw data sources (audio, text, images)
Specify desired actions or results
Break into sequential processing stages
Assign ML or traditional software to each step
Convert audio input to written text
Deep LearningClassify text into specific actions
Text ClassificationPerform requested action using software
Traditional EngineeringAutonomous vehicles demonstrate how complex AI systems are decomposed into specialized components.
Identifies objects in the vehicle's environment
Forecasts behavior of other entities
Determines optimal path to destination
Combines ML and rule-based systems for safe navigation
Executes physical vehicle actions
Complex AI systems are rarely monolithic. Break them into specialized components with clear interfaces.
Combine machine learning where patterns are complex with traditional software for deterministic rules.
Sometimes simplifying inputs (e.g., text instead of voice) can dramatically reduce complexity.
Start with available data and required outputs, then design transformations in between.
Complex AI projects require decomposition
Typical components after decomposition
Reduction in complexity through scope adjustment