jwkanggist / end-to-end-machine-learning-with-google-cloud

End to End Machine Learning with Google Cloud Platform

E2E-ML-GCP

Raw data

• Get the data using event driven cloud functions
  • functions: https://console.cloud.google.com/functions/details/us-central1/toBigQuery?project=ivikramtiwari&tab=general&duration=PT1H
• Store the data in BigQuery
  • bigquery: https://console.cloud.google.com/bigquery?project=ivikramtiwari&folder&organizationId&p=ivikramtiwari&d=census_consensus&t=adult_STAGING&page=table

Enriched data

• Cleanup the data using dataprep
  • dataprep: https://clouddataprep.com/flows/106821?recipe=514701&tab=recipe
• Run the cleanup job as a dataflow job at scale
  • dataflow: https://console.cloud.google.com/dataflow/jobsDetail/locations/us-central1/jobs/2018-11-09_09_28_35-15753238828455050695?project=ivikramtiwari

Experiments

• Launch a deep learning VM and then connect to it using SSH tunnel to access Jupyter lab
  • Deep Learning VMs
    • compute: https://console.cloud.google.com/compute/instancesDetail/zones/us-central1-f/instances/tf?project=ivikramtiwari
    • local: http://localhost:8080/lab?
• Launch your deep learning job on CMLE and on completion, get the model as output
  • cmle
  • job: https://console.cloud.google.com/mlengine/jobs/gde_summit_18_alpha_1?project=ivikramtiwari
  • model: https://console.cloud.google.com/mlengine/models/census?project=ivikramtiwari

More links

• Kaggle: https://www.kaggle.com/vikramtiwari
• slids: https://docs.google.com/presentation/d/1HpNoH2lmzsKgwmC2xDQrVTRuJS75Dtwvp3fZCb1qo74/present