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How to use
Machine Learning on
Google Cloud Platform
Giovanni Galloro - Customer Engineer, Google Cloud
galloro@google.com...
Machine Learning @ Google
Fast growth in ML adoption at Google
Google projects containing ML Models
2012 2013 2014 2015
3000
2000
1000
0
Used across...
Machine Learning in Google Products
improvement
to ranking quality
in 2+ years
#1
signal
for search ranking out
of hundreds
#3
Search
RankBrain - A deep neura...
Democratize AI
by making it
accessible,
fast and useful
for enterprises
and developers
An open source solution
Created by Google Brain team
Most popular ML project on Github
● Over 480 contributors
● 10,000 co...
Call our trained models as APIs:
our data + our models
Cloud
Vision API
Cloud
Translation API
Cloud Natural
Language API
C...
Machine Learning as an API
Call our trained models as APIs:
our data + our models
Cloud
Vision API
Cloud
Translation API
Cloud Natural
Language API
C...
Cloud Vision API
Label & web detection OCR Logo detection
Explicit content detectionCrop hintsLandmark detection
Cloud Natural Language
Extract
entities
Detect
sentiment
Analyze
syntax
Classify
content
Cloud AutoML
Call our trained models as APIs:
our data + our models
Cloud
Vision API
Cloud
Translation API
Cloud Natural
Language API
C...
Your training data
Generate predictions
with a REST API
AutoML
Train Deploy Serve
How it works
AutoML Vision
Let’s say I’m a
meteorologist
I want to predict
weather trends
and flight plans
from images of
clouds
AutoML Vision
There are many
different types of
clouds
AutoML Vision
They all indicate
different weather
patterns
AutoML Vision
Let’s try the Vision API
With AutoML Vision you can create custom
image classification models
What about other types of
data, like text?
Introducin...
Demo matching teachers with donors using
a Kaggle dataset from DonorsChoose.org
Tensorflow on
Cloud Machine Learning Engine
Call our trained models as APIs:
our data + our models
Cloud
Vision API
Cloud
Translation API
Cloud Natural
Language API
C...
Tools to build train and serve your own models
TensorFlow ML Engine
Hyperparameter tuning with HyperTune
● Automatic hyperparameter tuning
● Runs multiple trials in a training with
specified...
ASIC for TensorFlow
Designed by Google
Tensor Processing Unit
Cloud Datalab
Data stored in GCS, source code in
Cloud Repositories (ugit).
Notebook interface - Leverage existing
Jupyter...
Train and Deploy
Inputs
Train
model
Pre
processing
Asset
Creation
Distributed
training,
Hyper-parameter
tuning
Deploy: Inc...
ML End to End Pipeline
Inputs
Train
model
Pre
processing
Asset
Creation
Distributed
training,
Hyper-parameter
tuning
Deplo...
17+ Years of Tackling Big Data Problems
Google
Papers
20082002 2004 2006 2010 2012 2014 2015
GFS
Map
Reduce
Flume
Java
Ope...
17+ Years of Tackling Big Data Problems
Google
Papers
20082002 2004 2006 2010 2012 2014 2015
GFS
Map
Reduce
Flume
Java
Ope...
17+ Years of Tackling Big Data Problems
Google
Papers
20082002 2004 2006 2010 2012 2014 2015
GFS
Map
Reduce
Flume
Java
Ope...
Google
BigQuery
Fully Managed and Serverless
Google Cloud’s Enterprise Data Warehouse
for Analytics
Petabyte-Scale and Fas...
Where to store your analytics data
Inputs
Training &
Validation
Datasets
Deploy
API and
Versioning
Prediction
Clients
(Onl...
Data Engineering as part of you pipeline
Inputs
Train
model
Pre
processing
Asset
Creation
Deploy: Including
Model Versioni...
New ML Capabilities on GCP:
BigQuery ML
A new possibility with BQML:
Create ML Model directly in BigQuery
Data analysts and data scientists can
Use familiar SQL f...
39
BQML and SQL
● BQML provides a SQL interface for feature engineering, creation and execution of ML models
for data in B...
BigQuery ML: Further Extend ML Accessibility
Developer SQL Analyst Data Scientist Use cases and skills
TensorFlow and
Clou...
New (Upcoming) ML Capabilities:
Kubeflow & AI Hub
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
St...
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
St...
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
St...
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
St...
Storage
Framework
Tooling
UX
Model
Storage
Drivers
OS
Accelerator
Runtime
Framework
Tooling
UX
Drivers
OS
Accelerator
Runt...
Storage
Framework
Tooling
UX
Model
Storage
Framework
Tooling
UX
Model
Storage
Drivers
OS
Accelerator
Runtime
Framework
Too...
Empowering developers and data scientists
to run AI applications anywhere
Kubeflow Pipelines AI Hub
Collaboration
One-stop...
Kubeflow Pipelines
● Workbench to compose, deploy, and
manage end-to-end ML workflows
● Reliable and repeatable machine le...
AI Hub ALPHA
1. One stop AI catalog
Easily discover plug & play pipelines
& other content built by Google AI
and partners....
Thank You
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[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. 1. How to use Machine Learning on Google Cloud Platform Giovanni Galloro - Customer Engineer, Google Cloud galloro@google.com @ggalloro
  2. 2. Machine Learning @ Google
  3. 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. 4. Machine Learning in Google Products
  5. 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. 6. Democratize AI by making it accessible, fast and useful for enterprises and developers
  7. 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. 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. 9. Machine Learning as an API
  10. 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. 11. Cloud Vision API Label & web detection OCR Logo detection Explicit content detectionCrop hintsLandmark detection
  12. 12. Cloud Natural Language Extract entities Detect sentiment Analyze syntax Classify content
  13. 13. Cloud AutoML
  14. 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. 15. Your training data Generate predictions with a REST API AutoML Train Deploy Serve How it works
  16. 16. AutoML Vision Let’s say I’m a meteorologist
  17. 17. I want to predict weather trends and flight plans from images of clouds AutoML Vision
  18. 18. There are many different types of clouds AutoML Vision
  19. 19. They all indicate different weather patterns AutoML Vision
  20. 20. Let’s try the Vision API
  21. 21. With AutoML Vision you can create custom image classification models What about other types of data, like text? Introducing AutoML Natural Language
  22. 22. Demo matching teachers with donors using a Kaggle dataset from DonorsChoose.org
  23. 23. Tensorflow on Cloud Machine Learning Engine
  24. 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. 25. Tools to build train and serve your own models TensorFlow ML Engine
  26. 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. 27. ASIC for TensorFlow Designed by Google Tensor Processing Unit
  28. 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. 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. 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. 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. 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. 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. 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. 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. 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. 37. New ML Capabilities on GCP: BigQuery ML
  38. 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. 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. 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. 41. New (Upcoming) ML Capabilities: Kubeflow & AI Hub
  42. 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. 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. 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. 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. 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. 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. 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. 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. 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. 51. Thank You

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