Accelerating Machine Learning App Development with Kubeflow Pipelines (Cloud Next '19)

Kelvion New Plate Heat Exchanger


Building production-grade machine learning applications that run reliably and in a repeatable manner can be very challenging. Machine learning systems often need to orchestrate many steps, from data pre-processing and feature engineering, to model training, evaluation, and deployment. Teams need a structured way to develop, orchestrate, and run such multi-step pipelines, without sacrificing rapid prototyping and experimentation.

Find out how running Kubeflow on Google Cloud helped GOJEK to dramatically accelerate the speed at which they could deliver machine learning applications into production.

Accelerating Machine Learning App Development → http://bit.ly/2TZfO60

Watch more:
Next ’19 ML & AI Sessions here → https://bit.ly/Next19MLandAI
Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions

Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform

Speaker(s): Anand Iyer, Willem Pienaar

Session ID: MLAI211
product:Cloud ML Engine,AI,TensorFlow; fullname:Anand Iyer;


Duration: 46:38
Publisher: Google Cloud
You can watch this video also at the source.