Ruby Patterns for AI Developers: Procs, Lambdas, Closures, and Enumerable Magic

Ruby's functional programming features shine when building AI pipelines. Procs, lambdas, and closures let you encapsulate behavior, while Enumerable methods transform data with elegance. This article, part of the Ruby for AI series, shows how these patterns solve real problems in machine learning workflows. Understanding Procs and Lambdas Both Procs and lambdas are blocks of code you can store in variables and pass around. The differences matter for AI pipelines where data integrity is crucial. # A Proc - flexible argument handling normalize_proc = Proc . new { | value , min , max | ( value - min ) / ( max - min ). to_f } # A lambda - strict about arguments normalize_lambda = -> ( value , min , max ) { ( value - min ) / ( max - min ). to_f } # Both work for normalizing features data_point = 75.0 min_val = 0.0 max_val = 100.0 puts normalize_proc . call ( data_point , min_val , max_val ) # => 0.75 puts normalize_lambda . call ( data_point , min_val , max_val ) # => 0.75 # Procs ignore ex