google cloud machine learning

Cloud Machine Learning Google Cloud Platform

[email protected]( Business Development )

[email protected] (Customer Engineer)

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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

Beach

Woman

Pool

Coast

Water


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

MachineLearning

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

Obje

ctive

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 PredictionUS 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 assetAnalytics drive competitive advantageData at scale drives exponential complexity

Traditional BI does not scale to big dataMost organizations cannot capture all dataInformation growing faster than it can be leveraged

Retail Drivers - How Analytics Can Help?

DemandingCustomers

AggressiveCompetition

CostOptimization

ImproveExperience

UnderstandCustomers

FasterConversions

IncreaseSales

Customer Profiling Segmentation

Recommendations Cart Analysis

Market Hot Spotting

AssetPerformance

Social Media Analysis

Customer Personalization

Data AggregationMultiple Platforms

Location Planning

Catchment Analysis

Inventory Management

Logistics Management

Sales Forecasting

Impact Analysis

Risk Modeling


Transform Data into Actions

Exploration & CollaborationDatabases Storage

Data Preparation &

Processing Analytics

Advanced Analytics & Intelligence

Mobile apps

Sensors and devices

Web apps

Relational

Key-value

Document

SQL

Wide column

ObjectStream 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

Localtraining

Download

Mobileprediction

Batch

Online

Training

Prediction

Tooling

Datalab

Datalab

Tooling

UploadHosted 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 @ GoogleLevel 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 ValueUpdate 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 datasetSize 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

cloud.google.com