AI & Enterprise Melanie Warrick 

@nyghtowl 

@nyghtowl Multi years & Multi millions 

90% of Data Last 2 Years 

1.5GB / day avg person 1PB / day smart factory By 2020 

@nyghtowl Are you innovating with data? 

@nyghtowl Machine Learning 

@nyghtowl 

@nyghtowl “AI is the new electricity” - Andrew Ng ‘17 

@nyghtowl New tech is like magic Change => fear | excitement 

@nyghtowl “Science and engineering of making intelligent machines...” - John McCarthy ‘55 

@nyghtowl 

@nyghtowl 

@nyghtowl A.I. is the science of making technology smart 

@nyghtowl Artificial Intelligence Machine Learning Deep Learning 

@nyghtowl AlphaGo 

@nyghtowl Translate 

@nyghtowl Computer Vision 

@nyghtowl Sentiment Analysis 

@nyghtowl AI is not the destination How to apply it? 

@nyghtowl 

@nyghtowl Speech Recognition 

@nyghtowl Auction Site & Real-time Car Image Classifier 30K dealers | 5M cars Classification 20 - a couple minutes AUCNET 

@nyghtowl Compology Waste Removal Automation IoT Cameras & Vision Modeling Reduced pickups by ~50% 

@nyghtowl Largest South African pay-TV company Siloed data BigQuery Tailored subscriptions & real time offers real-time response 8.5M customers 27+ data sources 960M points consolidated MultiChoice 

@nyghtowl Goal Experts Data Solution 

@nyghtowl Goal Experts Data Solution 

@nyghtowl Define the Problem Revenue Growth Customer Satisfaction Responsiveness Sales Expenses Operations Processes Compliance Latency Product & Process Innovation 

@nyghtowl Structured Data Classification/ Regression ● Customer Churn Analysis ● Product Diagnostics ● Forecasting Recommendation ● Content Personalization ● Product X-Sells/Up-sells Anomaly Detection ● Fraud Detection ● Asset Sensor Diagnostics ● Log Metric Anomalies Unstructured Data Image Analytics ● Identify damaged shipments ● Explicit Content Classification ● Identify “styles” in images Text Analytics ● Call Center log analysis ● Language Identification ● Topic Classification ● Sentiment Analysis Machine Learning - Applications 

@nyghtowl Breakdown and Evolve 

@nyghtowl Growing Use of ML at Google Number of directories containing model description files 2012 2013 2014 2015 1500 1000 500 0 Used across products: 

@nyghtowl 40% savings 

@nyghtowl Goal Experts Data Solution 

@nyghtowl Capture Process & Train Store Analyze & Deploy Build the Solution 

@nyghtowl Capture Process & Train Store Analyze & Deploy Build the Solution 

163 Zetabytes (1 trillion gigabytes) of data by 2025 

Proprietary + Confidential Datacenter as a Computer 

@nyghtowl Capture Process Store Analyze & Deploy Capture Process & Train Store Build the Solution 

@nyghtowl Machine Learning Modeling Capture & Store Train Model Process Deploy & Serve Analyze & Improve 

@nyghtowl Make Data Coherent | Modeling 

@nyghtowl Neural Network = tons of multiply and add 

@nyghtowl Neural Network can extract hidden features from data 

@nyghtowl Neural Network is a function that can learn 

@nyghtowl 10 yrs => 1 day Simplify & Speed up Comprehension 

@nyghtowl Machine Learning Modeling Train Model Process Deploy & Serve Analyze & Improve Capture & Store 

@nyghtowl ML Data Lifecycle Solution Capture & Store Train Model Process Deploy & Serve Analyze & Improve Pub/ Sub Storage Cloud SQL Bigtable Dataflow Dataflow BigQuery TensorFlow Compute Engine ML Engine Datalab ML Engine Kubernetes Engine 

@nyghtowl End to End ML Pipeline Inputs Train model Pre processing Asset Creation Distributed training, Hyper-parameter tuning Deploy: Including Model Versioning REST API Prediction: load balanced, auto-scaled, scale-to-zero Model Remote Clients REST API call with input variables Asset Creation Model + Assets 

@nyghtowl Goal Experts Data Solution 

@nyghtowl Collaboration & Communication Business Engineers Researchers 

@nyghtowl Finding Solutions | Partners | Tools ➔ Proven experience? ➔ Temporary or full production? ➔ Scale & flexibility? ➔ Turnaround time? ➔ Success measure? ➔ Fit into existing systems? ➔ End to end? 

@nyghtowl End to End ML Pipeline Inputs Train model Pre processing Asset Creation Distributed training, Hyper-parameter tuning Deploy: Including Model Versioning REST API Prediction: load balanced, auto-scaled, scale-to-zero Model Remote Clients REST API call with input variables Asset Creation Model + Assets 

@nyghtowl Language API Vision API Translate API Speech API Remove Entry Barriers 

@nyghtowl Remote Input Predictions served, Load balanced, Auto-scaled, Scale-to-zero Pretrained ML Pipeline REST API Capture & Send Deploy & Serve 

@nyghtowl Translate Perfect translation Human Neural (GNMT) Phrase-based (PBMT) English > Spanish English > French English > Chinese Spanish > English French > Spanish Chinese > Spanish Translation model Translation quality old: PBMT new: GNMT 

@nyghtowl AutoML Vision Input Train Deploy Serve REST API Remote Clients AutoML Vision 

@nyghtowl Custom ML Modeling Collect & Store Train Model Process Deploy & Serve Analyze & Improve 

@nyghtowl Goal Experts Data Solution 

*Source: IDC Applied AI Focus & Simplify the Problem Utilize External Expertise 

@nyghtowl Thank you... Lindsay Cade Erin O’Connell Isabel Markl Eli Bixby Adam Shin Amy Unruh Sara Robinson June Andrews Tyler Benjamin Benjamin Chehebar 

@nyghtowl Resources ● Garnter Road to Enterprise AI: https://www.gartner.com/imagesrv/media-products/pdf/rage_frameworks/rage-frameworks-1- 34JHQ0K.pdf ● Google Cloud Platform: https://cloud.google.com/ ● Launchpad Studio (AI startup incubator): https://developers.google.com/programs/launchpad/studio/ ● Applied AI Examples: https://www.forbes.com/sites/robertadams/2017/01/10/10-powerful-examples-of-artificial-inte lligence-in-use-today/2/#1efe823e3c8b ● TensorFlow: https://www.tensorflow.org/ ● TensorFlow without a PhD: https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a- phd ● Deep Learning Training: www.fast.ai ● End-to-end ML with TensorFlow on GCP: https://codelabs.developers.google.com/codelabs/end-to-end-ml/index.html?index=..%2F..%2Fi ndex#0 

@nyghtowl Image References: ● iStock/diego_cervo ● iStock/phive2015 ● iStock/alexialex ● iSTock/Rinelle ● iStock/Noctiluxx ● iStock/VladimirFloyd ● istock/Besjunior ● https://commons.wikimedia.org/wiki/File:Stones_go.jpg ● http://www.ebss.co.jp/ebs/worldwide/service/implementation.htm ● https://www.sciencedaily.com/releases/2013/05/130522085217.htm ● https://images.yourstory.com/2016/09/Innovation-is-a-state-of-mind.png?auto=compress ● Rosenfeld Media | https://www.flickr.com/photos/rosenfeldmedia/6949089460 | Copyright and disclaimer notice: https://creativecommons.org/licenses/by/2.0/ | License notice: https://creativecommons.org/licenses/by/2.0/legalcode ● https://pixabay.com/en/backgammon-board-game-cube-strategy-1903940/ ● http://alphastockimages.com/ ● https://cloud.google.com/speech/ ● https://dillonspace.blogspot.com/2014/08/hitting-bullseye.html ● https://cdn.technologynetworks.com/tn/images/thumbnails/rectangle/cloud-based-research-informatics-improving-collabora tion-increasing-agility-and-reducing-operating-289722.png ● https://www.techemergence.com/machine-learning-in-genomics-applications/ ● https://www.biggerpockets.com/renewsblog/wp-content/uploads/2016/03/transcontinental-railroad.jpg ● http://goo.gl/fymPMI 

@nyghtowl Questions? @nyghtowl gcppodcast.com