Crossing the Chasm: Patterns to Develop, Operationalize, and Maintain ML Models (Cloud Next ’18)

Kelvion New Plate Heat Exchanger


In many organizations, data scientists develop machine learning models and data/ML engineers put them into production. The chasm between the two roles leads to many difficulties in moving models from development into production. These difficulties make it extremely difficult to maintain and enhance those models, a key requirement if ML models are used to drive the business. We describe four key concepts to keep in mind as you develop and operationalize machine learning models, and present a number of solutions (“patterns”) to realize these four concepts in practice.

Event schedule → http://g.co/next18

Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg
Next ‘18 All Sessions playlist → http://bit.ly/Allsessions

Subscribe to the Google Cloud channel! → http://bit.ly/NextSub


Duration: 47:7
Publisher: Google Cloud
You can watch this video also at the source.