FP & Scala: The Killer Combo for Machine Learning • Marek Kolodziej • YOW! 2015

Machine learning is now ubiquitous across industries (fraud detection, spam filtering, NLP, recommender systems, speech recognition, self-driving cars)

Traditional data science tools (R, Python) have limitations:

• Poor runtime performance
• Lack of proper memory management
• No referential transparency
• Limited scalability
• Imperative rather than functional approach

Scala advantages for machine learning:

• Better mapping between math and implementation
• Static typing helps catch errors early
• First-class functions
• Type inference
• Implicit conversions
• Supports both functional and object-oriented paradigms
• Excellent scalability through Spark integration

Key Scala ML libraries:

• Breeze for linear algebra and optimization
• Epic and Puck for NLP
• MLlib for Spark-based ML
• Wisp/Lightning for plotting
• Scala notebook for interactive development

Implementation benefits:

• Same code can run on both local collections and distributed Spark
• Clean functional implementations of algorithms
• Less boilerplate compared to Java
• Type safety while maintaining readability
• Easier maintenance of large codebases

Adoption challenges can be overcome through:

• Proper mentorship
• Showing practical benefits
• Demonstrating scalability advantages
• Leveraging existing JVM ecosystem
• Focusing on functional programming principles