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Fundamentals of Google Cloud: A Guided Tour

Fundamentals of Google Cloud: A Guided Tour

Whirlwind tour of various products available on Google Cloud Platform.

Florian Rathgeber

April 01, 2020

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  1. Confidential & Proprietary Fundamentals of Google Cloud: A Guided Tour

    Florian Rathgeber Site Reliability Engineer at Google Cloud @frathgeber follow me on twitter! Slide credits:
  2. Highly customisable Highly managed Compute Engine Cloud Marketplace App Engine

    Cloud Build Container Registry Kubernetes Engine Cloud Functions Cloud Run
  3. When you use Google Cloud, you’re using the network that

    powers... Seven cloud products with ONE BILLION users each
  4. Where do I store my data? In Memory Relational NoSQL

    Object Block Warehouse Cloud Memorystore Good for: Web/mobile apps, gaming Such as: Game state, user sessions Cloud SQL Good for: Web frameworks Such as: CMS, eCommerce Cloud Spanner Good for: RDBMS+scale, HA, HTAP Such as: User metadata, Ad/Fin/MarTech Cloud Datastore Good for: Hierarchical, mobile, web Such as: User profiles, key/val data Cloud Bigtable Good for: Heavy read + write, events Such as: AdTech, Financial, IoT Cloud Storage Good for: Binary or obj data (BLOB) Such as: Images, media, archive, backup Persistent Disk (GCE) Good for: Local VM file storage Such as: App data & binaries Big Query Good for: Data Warehouse Such as: Analytics, dashboards
  5. Big Data Lifecycle Google Analytics Premium Cloud Pub/Sub Capture Google

    Stackdriver Firebase Storage Transfer Service Use Data Scientists Business Analysts Cloud Datalab ... BigQuery Storage (tables) Cloud Bigtable (NoSQL) Cloud Storage (files) Store Cloud Dataflow BigQuery Analytics Analyze Cloud Dataproc ML Engine SQL Process Stream Real-time analytics Real-time dashboard Real-time alerts Batch Cloud Dataflow Cloud Dataprep
  6. Accelerating use of deep learning at Google Directories containing Deep

    Learning Models 2012 2013 2014 2015 3000 2000 1000 0 Used across products: 4000 2016 Unique project directories
  7. • • • • Learn more: What is the Tensor

    Flow machine intelligence platform?
  8. Framework GitHub Star Count TensorFlow 45581 scikit-learn 16423 Caffe 15882

    CNTK 9540 MXNet 8226 Torch 6344 Theano 5647 2010 2013 2015 2014 2016 2017 12500 25000 37500 50000 0
  9. Train your model with ML Engine trainingInput: scaleTier: CUSTOM masterType:

    complex_model_m workerType: complex_model_m parameterServerType: large_model workerCount: 9 parameterServerCount: 3 gcloud ml-engine jobs submit training $JOB_NAME \ --package-path $TRAINER_PACKAGE_PATH \ --module-name $MAIN_TRAINER_MODULE \ --job-dir $JOB_DIR \ --region $REGION \ --config config.yaml \
  10. 31

  11. © 2018 Google LLC. All rights reserved. An Algorithm that

    can create a Machine Learning Model Cloud AutoML allowing your developers to create high quality custom Models Introducing Cloud AutoML Human Data Labeling: For customers with no labeled training images, our in-house human labelers are available to review your custom instructions and label your images accordingly for model training. Production Ready: Use it immediately, Cloud AutoML is already deployed in GCP, Scales well with GCP large scale computation resource
  12. © 2018 Google LLC. All rights reserved. AutoML Vision Photo

    dataset Train Deploy Serve Generate predictions with a REST API AutoML to the rescue