The Machine Learning Journey with Google

Google Cloud Professional Services

The information, scoping, and pricing data in this presentation is for evaluation/discussion purposes only and is non-binding. For reference purposes, Google's standard terms and conditions for professional services are located at: https://enterprise.google.com/terms/professional-services.html. 1 What is machine learning?

2 Why all the attention now?

Topics How Google can support you inyour 3 journey to ML

4 Where to from here?

a branch of artificial intelligence

a way to solve problems without explicitly codifying the solution

a way to build systems that improve themselves over time

#1 #2 #3 #4

Democratization AI and ML will be core Specialized hardware Automation of ML of AI and ML competencies of for deep learning (e.g., MIT’s Data enterprises (CPUs → GPUs → TPUs) Science Machine & Google’s AutoML)

#5 #6 #7

Commoditization of Cloud as the platform ML set to transform deep learning for AI and ML banking and (e.g., TensorFlow) financial services

Used across products

New York

query = ‘Giants’

user location = ‘Bay Area’ ? user location = ‘New York’ ? user location = ‘other’ ?

results about results about results about SF Giants NY Giants giants

RankBrain helps process Google search results and provides more # 3 relevant search results for users signal for search, ranking out of hundreds

How is the business What caused the What is the sales What is the next performing? sales decline? forecast for next best action? quarter?

Describes state of the business

Helps to answer questions such as... How is the business How is our business performing? performing? Who are our best customers? What are best our selling products?

Analysis that helps to diagnose issues and root cause

Used to answer questions such as… What caused the sales decline? What caused a decline in sales? Why did a region miss its target?

Forward-looking analysis to anticipate the future

Helps to answer questions such as… What is the sales forecast for next What is our sales forecast for next quarter? quarter? Which customers are likely to default? Which prospects are most likely to buy our product?

Gives clear recommendation on the best course of action

Helps to answer questions such as… What is the next best action? How should we invest our money? What is the next best move? (AlphaGo) What is the best route to my destination?

BI + Reporting Machine Learning + AI

Low Sophistication High

Machine Learning and AI thrive on predictive and prescriptive analytics

Large Datasets Good ML Models Lots of Computation

“cat”

“dog”

INPUT OUTPUT “car”

“apple”

“flower”

Identify Develop Acquire + Build a Train the Apply and business hypothesis explore data model model scale problem

1 2 3 4 5 6

Manufacturing Retail Healthcare and Life Sciences

• Predictive maintenance or • Predictive inventory planning • Alerts and diagnostics from real-time condition monitoring • Recommendation engines patient data • Warranty reserve estimation • Upsell and cross-channel marketing • Disease identification and risk stratification • Propensity to buy • Market segmentation and targeting • Patient triage optimization • Demand forecasting • Customer ROI and lifetime value • Proactive health management • Process optimization • Healthcare provider sentiment analysis • Telematics

Energy, Feedstock and Financial Services Travel and Hospitality Utilities

• Risk analytics and regulation • Aircraft scheduling • Power usage analytics • Fraud detection • Dynamic pricing • Seismic data processing • Credit worthiness evaluation • Social media – consumer feedback • Carbon emissions and trading • Customer segmentation and interaction analysis • Customer-specific pricing • Cross-selling and up-selling • Customer complaint resolution • Smart grid management • Sales and marketing campaign • Traffic patterns and congestion • Energy demand and supply optimization management management

TensorFlow Cloud Machine Learning ML API

Customizable for data scientists Easy-to-use for non-ML engineers

Translate API

Vision API

Speech API

Natural Language API

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

1 Get your arms 2 Invest time in 3 Work with us. around big data understanding Best practices, Machine Learning partners to help you

Domain ML+AI algorithms Data Business + knowledge + = (Google) (customer) (customer) success

Customer Google Google Partner

Business case GCP, ML and AI expertise ML implementation + delivery Domain knowledge Guidance + advisory ML support + maintenance Data Best practices GCP expertise Engagement through project Thought leadership ML capability Commitment to build ML skills Training Capacity in-house ML model development (ASL)

1 2 3 4 5 Cloud Deploy ASL Solution Discover ML ML Training ML Deploy ML for MVM Development

Up to five day Google offers two Technical advisory to test Long term immersion Technical advisory engagement to options to train your feasibility of a specific on Google campus to help customer or identify business staff on Machine ML solution on GCP. working with our Partner build and problem, scope Learning, TensorFlow Google will assist client engineers to solve high deploy ML model on and priorities. and Google Cloud to create minimum viable impact business GCP. Platform ML model. challenges through with machine learning.

Operationalize Data Model Exploration

Cloud Deploy Machine Machine learning Learning for Production Feasibility study

Select Plan for algorithm Deployment

Present results Data Pipeline and Feature engineering

Cloud Deploy Machine Evaluate Build ML model Learning for MVM Cloud Deploy ML for MVM

Activity Description Format Deliverable

Data exploration Get guidance on data exploration methods and how to apply a Data exploration Onsite guidance subset of data exploration techniques to analyze your data set workshop

Get advice on ML feature engineering methods and use a Feature engineering Technical design subset of feature engineering techniques to design features Offsite guidance document for use cases to be implemented

Get guidance on the various ML algorithms, methods, and ML algorithms and First iteration/test techniques available. Learn how to split, train, test, and validate Onsite/Offsite technique guidance machine learning model data sets and evaluate the ML model

Identify requirements for integrating the ML model into the Implementation plan production workflow. Validate a model and build a business and architecture Offsite Project plan case for operationalization, design decisions, and steps guidance needed to operationalize the ML model on GCP

Tushar Chandra, Google Distinguished Engineer

Solving the biggest machine learning Better customer value challenges, alongside our customers

● Intensive ML training led by our best experts ● Faster time to value ● Long-term engagement with Google ML engineers ● Acquisition of best practices ● World-class, customer facilities on Google campuses ● Competitive advantage through bleeding edge solution from Google

Cloud Platform Machine Learning Tensor Flow Tailored Use Case Crash Course Crash Course Deep Dive Capstone Ideation ML Immersion Project Workshop Training Week 1 Week 2 Week 3 Week 4

1-2 Hours 2-3 Weeks 2-4 months 4-6 months Ongoing ML Solutions Development Qualification Tech Feasibility Terms + Solution Deployment Study/ Conditions development + + Ongoing Commercial Negotiations Eng. Support Viability Assessment

1 2 3 4 5

Chat Bots Image Regression Video Analysis Recommendation Fraud Detection

Conversational chat Google uses image Google is pioneering Google achieves Google uses machine bot AIs that are content analysis to the detection of intent unprecedented, learning to prevent contextually aware classify, detect auto within videos, based on double-digit gains in billions of dollars in of customer specific damage severity from millions of hours of user engagement by fraud across our data sets and photos video footage from using large scale products repositories YouTube. collaborative filtering

Business Impact Solvability Customer Readiness

A C-level sponsor inside your organization

A defined machine learning use case that can generate significant value

Data and the ability to move it to the Google Cloud

Team of data scientists / software engineers you can dedicate to the project

@speaker