[Giovanni Galloro] How to use machine learning on Google Cloud Platform
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[Giovanni Galloro] How to use machine learning on Google Cloud Platform
- 1. How to use Machine Learning on Google Cloud Platform Giovanni Galloro - Customer Engineer, Google Cloud galloro@google.com @ggalloro
- 2. Machine Learning @ Google
- 3. Fast growth in ML adoption at Google Google projects containing ML Models 2012 2013 2014 2015 3000 2000 1000 0 Used across products: 4000 2016 Uniqueprojectdirectories Time
- 4. Machine Learning in Google Products
- 5. improvement to ranking quality in 2+ years #1 signal for search ranking out of hundreds #3 Search RankBrain - A deep neural network for search ranking machine learning for search engines
- 6. Democratize AI by making it accessible, fast and useful for enterprises and developers
- 7. An open source solution Created by Google Brain team Most popular ML project on Github ● Over 480 contributors ● 10,000 commits in 12 months Multiple deployment options: ● Mobile, Desktop, Server, Cloud ● CPU, GPU
- 8. Call our trained models as APIs: our data + our models Cloud Vision API Cloud Translation API Cloud Natural Language API Cloud Speech API Cloud Video Intelligence API Build your own models: your data + your model Cloud ML Engine AutoML: your data + our model Cloud AutoML Main ML Capabilities on GCP
- 9. Machine Learning as an API
- 10. Call our trained models as APIs: our data + our models Cloud Vision API Cloud Translation API Cloud Natural Language API Cloud Speech API Cloud Video Intelligence API Build your own models: your data + your model Cloud ML Engine AutoML: your data + our model Cloud AutoML Main ML Capabilities on GCP
- 11. Cloud Vision API Label & web detection OCR Logo detection Explicit content detectionCrop hintsLandmark detection
- 13. Cloud AutoML
- 14. Call our trained models as APIs: our data + our models Cloud Vision API Cloud Translation API Cloud Natural Language API Cloud Speech API Cloud Video Intelligence API Build your own models: your data + your model Cloud ML Engine AutoML: your data + our model Cloud AutoML Main ML Capabilities on GCP
- 15. Your training data Generate predictions with a REST API AutoML Train Deploy Serve How it works
- 16. AutoML Vision Let’s say I’m a meteorologist
- 17. I want to predict weather trends and flight plans from images of clouds AutoML Vision
- 18. There are many different types of clouds AutoML Vision
- 19. They all indicate different weather patterns AutoML Vision
- 20. Let’s try the Vision API
- 21. With AutoML Vision you can create custom image classification models What about other types of data, like text? Introducing AutoML Natural Language
- 22. Demo matching teachers with donors using a Kaggle dataset from DonorsChoose.org
- 23. Tensorflow on Cloud Machine Learning Engine
- 24. Call our trained models as APIs: our data + our models Cloud Vision API Cloud Translation API Cloud Natural Language API Cloud Speech API Cloud Video Intelligence API Build your own models: your data + your model Cloud ML Engine AutoML: your data + our model Cloud AutoML Main ML Capabilities on GCP
- 25. Tools to build train and serve your own models TensorFlow ML Engine
- 26. Hyperparameter tuning with HyperTune ● Automatic hyperparameter tuning ● Runs multiple trials in a training with specified HP metrics ● Gaussian process to optimize the trials HyperParam #1 Objective Want to find this Not these
- 27. ASIC for TensorFlow Designed by Google Tensor Processing Unit
- 28. Cloud Datalab Data stored in GCS, source code in Cloud Repositories (ugit). Notebook interface - Leverage existing Jupyter modules and knowledge. Scalable (CPU, GPUs & memory) & cost-effective (pay as you use) Dataproc (Spark) integration for large scale exploratory data analysis. BigQuery “magic” and Cloud Storage integration. Cloud Datalab
- 29. Train and Deploy Inputs Train model Pre processing Asset Creation Distributed training, Hyper-parameter tuning Deploy: Including Model Versioning Cloud ML HTTP Request Remote Clients REST API call with input variables Feature Creation ModelModel
- 30. ML End to End Pipeline Inputs Train model Pre processing Asset Creation Distributed training, Hyper-parameter tuning Deploy: Including Model Versioning Cloud MLRemote Clients REST API call with input variables Feature Creation ModelModel Preprocess & Feature Creation Prediction
- 31. 17+ Years of Tackling Big Data Problems Google Papers 20082002 2004 2006 2010 2012 2014 2015 GFS Map Reduce Flume Java Open Source 2005 Google Cloud Products BigTable Dremel PubSub Millwheel TensorflowSpanner 2016
- 32. 17+ Years of Tackling Big Data Problems Google Papers 20082002 2004 2006 2010 2012 2014 2015 GFS Map Reduce Flume Java Open Source 2005 Google Cloud Products BigTable Dremel PubSub Millwheel TensorflowSpanner 2016
- 33. 17+ Years of Tackling Big Data Problems Google Papers 20082002 2004 2006 2010 2012 2014 2015 GFS Map Reduce Flume Java Open Source 2005 Google Cloud Products BigQuery Pub/Sub Dataflow Bigtable BigTable Dremel PubSub Millwheel TensorflowSpanner ML 2016 Spanner
- 34. Google BigQuery Fully Managed and Serverless Google Cloud’s Enterprise Data Warehouse for Analytics Petabyte-Scale and Fast Convenience of SQL Encrypted, Durable and Highly Available
- 35. Where to store your analytics data Inputs Training & Validation Datasets Deploy API and Versioning Prediction Clients (Online Systems) REST API call with input variables Trained Models Training Serving Preprocess & Feature Creation Preprocess & Feature Creation Train/Tune Model Integrate your data into BiqQuery to perform Exploratory Data Analysis at Scale
- 36. Data Engineering as part of you pipeline Inputs Train model Pre processing Asset Creation Deploy: Including Model Versioning Cloud MLRemote Clients REST API call with input variables Feature Creation ModelModel Preprocess & Feature Creation Prediction How to do your data engineering and when to use what?
- 37. New ML Capabilities on GCP: BigQuery ML
- 38. A new possibility with BQML: Create ML Model directly in BigQuery Data analysts and data scientists can Use familiar SQL for machine learning Train models over all their data in BigQuery Not worry about hypertuning or feature transformations 1 2 3
- 39. 39 BQML and SQL ● BQML provides a SQL interface for feature engineering, creation and execution of ML models for data in BQ ○ SQL statements for creation and execution of models ○ Automated hyperparameter tuning (transparent tuning) ○ Constructs for pre-processing for feature engineering ● The SQL constructs is part of GoogleSQL standard CREATE MODEL my_models.car_accidents OPTIONS(type=‘log_reg’) AS SELECT speed, age, ..., bad_accident as label FROM input_table); SELECT label FROM ml.PREDICT( MODEL my_models.car_accidents, (SELECT speed, age, ... FROM input_table)); CREATE MODEL my_models.car_accidents OPTIONS(type=‘log_reg’) AS SELECT speed, age, ..., bad_accident as label FROM input_table); SELECT label FROM ml.PREDICT( MODEL my_models.car_accidents, (SELECT speed, age, ... FROM input_table));
- 40. BigQuery ML: Further Extend ML Accessibility Developer SQL Analyst Data Scientist Use cases and skills TensorFlow and CloudML Engine ● Build and deploy state-of-art custom models ● Requires deep understanding of ML and programming BigQuery ML ● Build and deploy custom models using SQL ● Requires only basic understanding of ML AutoML and CloudML APIs ● Build and deploy Google-provided models for standard use cases ● Requires almost no ML knowledge
- 41. New (Upcoming) ML Capabilities: Kubeflow & AI Hub
- 42. Storage Drivers OS Accelerator Runtime Framework Tooling UX Storage Drivers OS Accelerator Runtime Framework Tooling UX Storage Drivers OS Accelerator Runtime Framework Tooling UX HW HW HW Model Model Model Laptop Training Rig Cloud
- 43. Storage Drivers OS Accelerator Runtime Framework Tooling UX Storage Drivers OS Accelerator Runtime Framework Tooling UX Storage Drivers OS Accelerator Runtime Framework Tooling UX HW HW HW Model Model Model Laptop Training Rig Cloud
- 44. Storage Drivers OS Accelerator Runtime Framework Tooling UX Storage Drivers OS Accelerator Runtime Framework Tooling UX Storage Drivers OS Accelerator Runtime Framework Tooling UX HW HW HW Model Model Model Laptop Training Rig Cloud Kubeflow
- 45. Storage Drivers OS Accelerator Runtime Framework Tooling UX Storage Drivers OS Accelerator Runtime Framework Tooling UX Storage Drivers OS Accelerator Runtime Framework Tooling UX HW HW HW Model Model Model Laptop Training Rig Cloud Kubeflow Kubeflow
- 46. Storage Framework Tooling UX Model Storage Drivers OS Accelerator Runtime Framework Tooling UX Drivers OS Accelerator Runtime Storage Drivers OS Accelerator Runtime Framework Tooling UX HW HW HW Model Model Laptop Training Rig Cloud Kubeflow Kubeflow Kubeflow
- 47. Storage Framework Tooling UX Model Storage Framework Tooling UX Model Storage Drivers OS Accelerator Runtime Framework Tooling UX Drivers OS Accelerator Runtime Drivers OS Accelerator Runtime HW HW HW Model Laptop Training Rig Cloud Kubeflow Kubeflow Kubeflow Kubeflow
- 48. Empowering developers and data scientists to run AI applications anywhere Kubeflow Pipelines AI Hub Collaboration One-stop catalog of pre-built pipelines & AI components Reuse Composable and reusable pipelines 1 2
- 49. Kubeflow Pipelines ● Workbench to compose, deploy, and manage end-to-end ML workflows ● Reliable and repeatable machine learning for all users ● Rapid experimentation ● Integrated TFX tools for bias detection ● Open and hybrid
- 50. AI Hub ALPHA 1. One stop AI catalog Easily discover plug & play pipelines & other content built by Google AI and partners. 2. Enterprise-grade sharing controls Host pipelines and ML content with private sharing controls within an enterprise to foster reuse within organizations. 3. Easy deployment on GCP and hybrid Deploy pipelines via Kubeflow on GCP and on premise.
- 51. Thank You