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:
2. Florian Site Reliability Engineer Google Cloud • • • •
4. Proprietary + Confidential
5. Proprietary + Confidential
6. Compute Running your code
7. Virtual Machines Containers Serverless
9. Highly customisable Highly managed Compute Engine Cloud Marketplace App Engine Cloud Build Container Registry Kubernetes Engine Cloud Functions Cloud Run
10. When you use Google Cloud, you’re using the network that powers... Seven cloud products with ONE BILLION users each
11. Edge points of presence >100 Network sea cable investments Google global cache edge nodes (>800) Network
12. Storage Storing your data
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
14. Structured Data (Relational) Cloud SQL Spanner Proprietary + Confidential
15. Unstructured Data (NoSQL) Datastore Firestore BigTable Proprietary + Confidential Photo by Sebas Ribas on Unsplash
16. Object Store (Blobs) Cloud Storage Proprietary + Confidential
17. Proprietary + Confidential
18. Big Data Extracting Insights
19. BigTable Dremel Colossus Flume Megastore Spanner PubSub Millwheel Cloud Dataflow Cloud Dataproc MapReduce
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
21. Machine Learning Learning from your data
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
23. Use our pre-trained models Create & serve your custom models
25. • • • • Learn more: What is the Tensor Flow machine intelligence platform?
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
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 \
29. Monitor ML Engine job on Cloud Console
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
33. © 2018 Google LLC. All rights reserved. AutoML Vision Photo dataset Train Deploy Serve Generate predictions with a REST API AutoML to the rescue
34. Resources Main page cloud.google.com Console console.cloud.google.com Codelabs g.co/codelabs/cloud Training cloud.google.com/training
35. cloud.google.com/free
36. Confidential & Proprietary Thank you! Florian Rathgeber @frathgeber follow me on twitter!