Are you trying to orchestrate enterprise-grade Data Science and Machine learning workloads with high scalability, performance and manageability? In this video, Kristin Kim, a Cloud Technical Resident at Google, walks through customer scenarios, solution approaches, and step-by-step creation on the Google Cloud Platform console.
Check out the code sample here → [ Ссылка ]
Read the blog post here → [ Ссылка ]
If you are interested in running data science batch workloads on Serverless Spark, check out the following labs and resources to get started:
Dataproc Serverless → [ Ссылка ]
Apache Spark and Jupyter Notebooks on Cloud Dataproc → [ Ссылка ]
Serverless Spark Workshop → [ Ссылка ]
If you are interested in orchestrating Notebooks on Ephemeral Dataproc clusters via Cloud Composer, check out this orchestrator → [ Ссылка ]
Subscribe to Google Cloud Tech → [ Ссылка ]
