In practice, large-scale applied Machine Learning (ML) requires substantial infrastructure and systems engineering investment. Learn about scaling machine learning in heterogeneous language environments across several domains and at all stages of a project’s lifecycle, including ad-hoc exploration, preparing training data, model development, and robust production deployment.