Kotlin for Machine Learning

Explore the potential of Kotlin for machine learning in this 48-minute conference talk from MLCon. Discover how Kotlin can be utilized throughout the entire machine learning project lifecycle, from data cleaning and feature extraction to model deployment and serving. Learn about various tools in the Kotlin ecosystem specifically designed for data science projects, including Kotlin in Jupyter notebooks, visualization capabilities, and dataframe handling. Investigate whether Kotlin can empower data scientists to create production-ready models beyond proof of concepts. Compare Kotlin's strengths to Python's established position in the machine learning field, and gain insights into why considering alternative languages for ML projects might be beneficial. Speaker Hauke Brammer provides a comprehensive overview of Kotlin's applications in machine learning, challenging the notion that Python is the only viable option for ML development.