Petabytes of satellite imagery contain valuable insights into scientific and economic activity around the globe. In order to turn geospatial data into decisions, Descartes Labs has built an end-to-end data processing and modeling platform in Google Cloud. We leverage tools including Kubeflow Pipelines in our model building process to enable efficient experimentation, orchestrate complicated workflows, maximize repeatability and reuse, and deploy at scale. This talk will walk through implementing machine learning workflows in Kubeflow Pipelines, covering successes and challenges of using these tools in practice.
ML With Kubeflow Pipelines → http://bit.ly/2WQBZgS
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): Kyle Story, Faustine Li
Session ID: MLAI200
product:Cloud ML Engine,Cloud TPU,AI,TensorFlow;
Publisher: Google Cloud
You can watch this video also at the source.