Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference

Train, predict, and serve: How to put your machine learning model into production

Aki Ariga (Cloudera)
1:45pm2:25pm Wednesday, December 6, 2017
Average rating: ***..
(3.00, 1 rating)

Who is this presentation for?

  • Data scientists and engineers

Prerequisite knowledge

  • A basic understanding of machine learning (how to build a predictive model, what ML pipeline is, etc.)

What you'll learn

  • Learn best practices and common patterns for putting machine learning deployments into production


Adopting a machine learning system is an essential step for enterprise companies to progress to the next stage of their business. However, machine learning systems tend to be complex, because they depend on different languages, libraries, or frameworks, such as scikit-learn, TensorFlow, and XGBoost. As a result, there are many challenges for building machine learning system in production, including determining which architecture is best for which use case, how to deploy your predictive models, and how to move from development and to a production environment.

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.

Photo of Aki Ariga

Aki Ariga


Aki Ariga is a field data scientist at Cloudera, where he works on service development with machine learning and natural language processing. His work has included researching spoken dialogue systems, building a large corpus analysis system, and developing services such as recipe recommendations. Aki is a sparklyr contributor. He organizes several tech communities in Japan, including Ruby, machine learning, and Julia.