Google Cloud Machine Learning

Cloud Machine Learning
Google Cloud Platform
anujash@google.com( Business Development )
kunadeo@google.com (Customer Engineer)

Data is exploding.
And smart companies are taking advantage.

Unstructured data accounts for
90% of enterprise data*
Cloud Machine Learning help you
make sense of it
*Source: IDC

Confidential & ProprietaryGoogle Cloud Platform 4
What is Machine Learning?
Data Algorithm Insight

Machine Learning @ Google

Google Translate

Enterprise Predictive Analytics Challenges
Data access to a variety
of data sources.
Develop and build
analytic models.
Data preparation,
exploration and visualization.
Deploy models and integrate
them into business processes
and applications.
High performance and scalability
for both development
and deployment.
Perform platform, project
and model management.

Data Warehouse is the Foundation
of Something Bigger
Data
Warehouses/Lakes
Machine Intelligence Predictive
+
Prescriptive
Analytics
=
Advanced
Analytics
Cloud
On
Premises
Machine
Learning
APIs
Train
Your Own
Models

Machine Learning Use Cases
• Predictive maintenance or condition
monitoring
• Warranty reserve estimation
• Propensity to buy
• Demand forecasting
• Process optimization
Manufacturing
• Predictive inventory planning
• Recommendation engines
• Upsell and cross-channel marketing
• Market segmentation and targeting
• Customer ROI and lifetime value
Retail
• Alerts and diagnostics from real-time
patient data
• Disease identification and risk satisfaction
• Patient triage optimization
• Proactive health management
• Healthcare provider sentiment analysis
Healthcare and Life Sciences
• Aircraft scheduling
• Dynamic pricing
• Social media – consumer feedback and
interaction analysis
• Customer complaint resolution
• Traffic patterns and congestion
management
Travel and Hospitality
• Risk analytics and regulation
• Customer Segmentation
• Cross-selling and up-selling
• Sales and marketing campaign
management
• Credit worthiness evaluation
Financial Services
• Power usage analytics
• Seismic data processing
• Carbon emissions and trading
• Customer-specific pricing
• Smart grid management
• Energy demand and supply optimization
Energy, Feedstock and Utilities

Why So Little Machine Learning Apps Out There?
• Building and scaling machine learning infrastructure is
hard
• Operating production ML system is time consuming and
expensive

Building Smart Applications Today
Technology Operationalization Tooling
Difficult to scale
Many choices for different
use cases
Using latest technology (e.g.
DNN) is hard
Complex data pipelines
Managing ML infra takes
away time from actually
doing ML
Many models to manage
Complex dev pipeline with
many combinations of
tools/libraries
Not fully interactive
developer experience -
collaboration/sharing is hard

Introducing Cloud Machine Learning
● Fully managed service
● Train using a custom TensorFlow graph
for any ML use cases
● Training at scale to shorten dev cycle
● Automatically maximize predictive
accuracy with HyperTune
● Batch and online predictions, at scale
● Integrated Datalab experience

Cloud Datalab
● Interactively explore data
● Define features with rich visualization support
● Launch training and evaluation
● ML lifecycle support
● Combine code, results, visualizations &
documentation in notebook format
● Share results with your team
● Pick from a rich set of tutorials & samples to
learn and get started with your project

Powerful Machine Learning Algorithm
● Convolutional Neural Network for image classification
● Recursive Neural network for text sentiment analysis
● Linear regression at scale to predict consumer action
(purchase prediction, churn analysis)
● And unlimited variety of algorithms you can build using TensorFlow

Automatically tune your model with HyperTune
● Automatic hyperparameter tuning
service
● Build better performing models
faster and save many hours of
manual tuning
● Google-developed search
algorithm efficiently finds better
hyperparameters for your
model/dataset
HyperParam #1
Objective
Want to find this
Not these
HyperParam
#2

Integrated with GCP Products
● Access data that is stored in GCS or BigQuery
● Save trained models to GCS
● Preprocess largest datasets (TB) using Dataflow
● Orchestrate ML workflow as a Dataflow pipeline
● Analyze data and interactively develop ML models in Datalab

Fully Managed Machine Learning Services
● Scalable and distributed training infrastructure for your largest
data sets
● Scalable prediction infrastructure that can serve very large traffic
● Managed no-ops infrastructure handles provisioning, scaling,
and monitoring so that you can focus on building your models
instead of handling clusters

Pay As You Go and Inexpensive
Tier Price
Regular
$0.1 / 1K
+$0.40/Node Hour
Large volume
$0.05/1K
+$0.40/Node Hour
after 100M/month
Training Prediction
US Europe / Asia
1 ML training unit $0.49 $0.54
Tier Training unit
per hour
Characteristics
BASIC 1 A single worker instance. This tier is suitable for
learning how to use Cloud ML, and for experimenting
with new models using small datasets.
STANDARD
_1
10 Mid-size cluster with many workers and a few
parameter servers for medium scale distributed training
PREMIUM_
1
75 Larger cluster with a large number of workers with
many parameter servers. Suitable for large scale job
with complex and larger models
CUSTOM Custom Fine tune the number of workers, parameter servers
and machine types

Do You Have The Right Visibility?
** Ventura Research Report** Ventura Research Report
34%
51%
71%
Of retail companies are satisfied
with the processes they use to
create analytics.
Of retailers are still using
spreadsheets as their primary
data analysis tools
Find challenge in data
sharing
45%
Are not effectively using
data to personalize
marketing communications 42%
Are not able to link data
together at the individual
customer level
Largest ObstacleRetail Analytic Trends

Challenges
Difficulty Understanding Customers
What drives the customers buying
habits?
What products do customers prefer to
buy and what related products?
What causes customers to not buy?
Customizing The Experience
How can I ensure each customer sees
the products they’re interested in as
quickly as possible?
How can my eCommerce app react in
real-time to customer actions?
Data Aggregation & Processing
Need for a large, scalable storage
solution to aggregate, store, and serve
applications
Compute capacity required to churn
and derive insights constantly
increasing
Analytics & Machine Learning can be
resource hogs
Key Takeaways Data is a core business asset
Analytics drive competitive advantage
Data at scale drives exponential complexity
Traditional BI does not scale to big data
Most organizations cannot capture all data
Information growing faster than it can be leveraged

Retail Drivers - How Analytics Can Help?
Demanding
Customers
Aggressive
Competition
Cost
Optimization
Improve
Experience
Understand
Customers
Faster
Conversions
Increase
Sales
Customer Profiling
Segmentation
Recommendations
Cart Analysis
Market
Hot Spotting
Asset
Performance
Social Media
Analysis
Customer
Personalization
Data Aggregation
Multiple Platforms
Location Planning
Catchment Analysis
Inventory
Management
Logistics
Management
Sales
Forecasting
Impact
Analysis
Risk
Modeling

Transform Data into Actions
Exploration &
Collaboration
Databases Storage
Data
Preparation &
Processing
Analytics
Advanced
Analytics &
Intelligence
Mobile apps
Sensors and
devices
Web apps
Relational
Key-value
Document
SQL
Wide column
Object
Stream
processing
Batch
processing
Data
preparation
Federated
query
Data catalog
Data
exploration
Data
visualization
Developers
Data scientists
Business
analysts
Development
environment
for Machine
Learning
Pre-Trained
Machine
Learning
models
Data
Ingestion
Messaging
Logs

Transform Data into Actions
Data
Preparation &
Processing
Cloud Dataflow
Cloud Dataproc
Exploration &
Collaboration
Google
BigQuery
Cloud Datalab
Google
Analytics 360
Cloud Dataproc
Mobile apps
Sensors and
devices
Web apps
Developers
Data scientists
Business
analysts
Data Ingestion
Cloud Pub/Sub
App Engine
Databases/
Storage
Cloud SQL
Cloud Bigtable
Cloud
Datastore
Cloud Storage
Analytics
Google BigQuery
Google
Analytics 360
Cloud Dataproc
Google Drive
Advanced
Analytics &
Intelligence
Cloud Machine
Learning
Translate API
Vision API
Speech API

Use Your Own Data to Train Models
BETA
BETA
GAGA
Cloud Datalab
Cloud Machine Learning
Cloud Storage Google BigQuery Develop/Model/Test

HTTP request
Use your own data to train models
Pre-ProcessingData Storage
Training flow
Prediction flow
Local
training
Download
Mobile
prediction
Batch
Online
Training
Prediction
Tooling
Datalab
Datalab
Tooling
Upload
Hosted Model

Automatically
categorize, and
automatically
extract value
Evaluate the model by
applying it against
additional manually
categorized data, correct
and tune
Capture thousands of
examples of correct
evaluations for that
categorization, and use
them to train an ML model
Identify categorizations
that provide value,
categories you’re
already evaluating for
by hand today
1 2 3 4
Machine Intelligence is Already Making a Huge
Difference and There are Many, Many More Opportunities

Machine Learning @ Google
Level 200

The point of ML is to make predictions
Input Feature Predicted Value
Model

Tensorflow helps you “train” models
Input Feature Predicted Value
Model
True Value
Update model
based on Cost
Cost

Democratizing machine learning
App DeveloperData Scientist
CloudML
Build custom modelsUse/extend OSS SDK
Scale, No-ops
Infrastructure
ML APIs
Vision API
Speech API
Use pre-built models
Translate API
ML researcher
Language API

Beyond Tensorflow
Size of dataset
Size of NN
Scale of
Compute
Problem
Accuracy
CloudML ( )
Deep networks
TensorFlow
Processing
Units (TPUs)
Distributed
No-ops
https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html
ML APIs
Vision API
Speech API
Translate API
Language API

ML APIs are simply REST calls and can be made
from any language or framework
sservice = build('speech', 'v1beta1', developerKey=APIKEY)
response = sservice.speech().syncrecognize(
body={
'config': {
'encoding': 'LINEAR16',
'sampleRate': 16000
},
'audio': {
'uri': 'gs://cloud-training-demos/vision/audio.raw'
}
}).execute()
print response
Data on Cloud Storage

Google Cloud Machine Learning

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