Aki Ariga explains how to put your machine learning model into production, discusses common issues and obstacles you may encounter, and shares best practices and typical architecture patterns of deployment ML models with example designs from the Hadoop and Spark ecosystem using Cloudera Data Science Workbench.

Python's popularity for data science use cases has skyrocketed in recent years due to its ease of use, great developer and user community, and solid core of scientific libraries. Christine Doig explores data science and the state of the Python ecosystem and helps navigate the large amount of open source libraries available for data science in Python, providing a map to guide you on the journey.