Fundamentals of Google Cloud: A Guided Tour

Whirlwind tour of various products available on Google Cloud Platform.

Transcript

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:
   

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2. Florian
   Site Reliability Engineer
   Google Cloud
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4. Proprietary + Confidential
   

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5. Proprietary + Confidential
   

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6. Compute
   Running your code
   

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7. Virtual Machines Containers Serverless
   

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9. Highly customisable Highly managed
   Compute
   Engine
   Cloud
   Marketplace
   App
   Engine
   Cloud Build Container
   Registry
   Kubernetes
   Engine
   Cloud
   Functions
   Cloud
   Run
   

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10. When you use Google Cloud,
    you’re using the network
    that powers...
    Seven cloud products with
    ONE BILLION
    users each
    

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11. Edge points of presence >100
    Network sea cable investments
    Google global cache edge nodes (>800)
    Network
    

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12. Storage
    Storing your data
    

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13. 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
    

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14. Structured Data
    (Relational)
    Cloud SQL
    Spanner
    Proprietary + Confidential
    

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15. Unstructured Data
    (NoSQL)
    Datastore
    Firestore
    BigTable
    Proprietary + Confidential
    Photo by Sebas Ribas on Unsplash
    

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16. Object Store
    (Blobs)
    Cloud Storage
    Proprietary + Confidential
    

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17. Proprietary + Confidential
    

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18. Big Data
    Extracting Insights
    

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19. BigTable Dremel
    Colossus
    Flume
    Megastore
    Spanner
    PubSub
    Millwheel
    Cloud
    Dataflow
    Cloud
    Dataproc
    MapReduce
    

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20. 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
    

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21. Machine Learning
    Learning from your data
    

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22. 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
    

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23. Use our pre-trained models Create & serve your custom
    models
    

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    Learn more: What is the Tensor Flow machine intelligence platform?
    

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26. 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
    

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28. 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 \
    

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29. Monitor ML Engine job on Cloud Console
    

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32. © 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
    

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33. © 2018 Google LLC. All rights reserved.
    AutoML Vision
    Photo dataset
    Train Deploy Serve
    Generate predictions
    with a REST API
    AutoML to the rescue
    

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34. Resources
    Main page cloud.google.com
    Console console.cloud.google.com
    Codelabs g.co/codelabs/cloud
    Training cloud.google.com/training
    

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35. cloud.google.com/free
    

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36. Confidential & Proprietary
    Thank you!
    Florian Rathgeber
    @frathgeber
    follow me
    on twitter!
    

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