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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? © 2019 Google LLC. All rights reserved. What is machine0 learning? 1 Machine learning is... 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 © 2019 Google LLC. All rights reserved. Key trends in artificial intelligence and machine learning #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 © 2019 Google LLC. All rights reserved. Use of machine learning is rapidly accelerating Used across products © 2019 Google LLC. All rights reserved. Google Translate © 2019 Google LLC. All rights reserved. Why all the attention0 now? 2 Machine learning allows us to solve problems without codifying the solution. © 2019 Google LLC. All rights reserved. San Francisco New York © 2019 Google LLC. All rights reserved. Machine learning scales better than hand-coded rules query = ‘Giants’ user location = ‘Bay Area’ ? user location = ‘New York’ ? user location = ‘other’ ? results about results about results about SF Giants NY Giants giants © 2019 Google LLC. All rights reserved. # 1 improvement to ranking quality in 2+ years machine learning for search engines RankBrain helps process Google search results and provides more # 3 relevant search results for users signal for search, ranking out of hundreds © 2019 Google LLC. All rights reserved. Analytics projects fall into four categories How is the business What caused the What is the sales What is the next performing? sales decline? forecast for next best action? quarter? © 2019 Google LLC. All rights reserved. What is descriptive analytics? 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? © 2019 Google LLC. All rights reserved. What is diagnostic analytics? 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? © 2019 Google LLC. All rights reserved. What is predictive analytics? 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? © 2019 Google LLC. All rights reserved. What is prescriptive analytics? 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? © 2019 Google LLC. All rights reserved. Traditional BI and reporting do descriptive and diagnostic analytics BI + Reporting Machine Learning + AI Low Sophistication High Machine Learning and AI thrive on predictive and prescriptive analytics © 2019 Google LLC. All rights reserved. Keys to successful ML Large Datasets Good ML Models Lots of Computation © 2019 Google LLC. All rights reserved. Deep neural networks is an important technology we use “cat” “dog” INPUT OUTPUT “car” “apple” “flower” © 2019 Google LLC. All rights reserved. To build a machine learning model Identify Develop Acquire + Build a Train the Apply and business hypothesis explore data model model scale problem 1 2 3 4 5 6 © 2019 Google LLC. All rights reserved. 10^170 30M Possible positions Trained games © 2019 Google LLC. All rights reserved. Machine learning use cases 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 © 2019 Google LLC. All rights reserved. How Google can support you in your journey 0to ML 3 A full spectrum of ML offerings from Google 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 © 2019 Google LLC. All rights reserved. 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 © 2019 Google LLC. All rights reserved. Three steps for success with machine learning 1 Get your arms 2 Invest time in 3 Work with us. around big data understanding Best practices, Machine Learning partners to help you © 2019 Google LLC. All rights reserved. Google is a catalyst for your ML journey Domain ML+AI algorithms Data Business + knowledge + = (Google) (customer) (customer) success © 2019 Google LLC. All rights reserved. Machine learning is a team effort 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) © 2019 Google LLC. All rights reserved. We can help you get started 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. © 2019 Google LLC. All rights reserved. Monitor Model Define business use cases Cloud Discover: 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 © 2019 Google LLC. All rights reserved. Advanced Solutions Lab © 2019 Google LLC. All rights reserved. “When applied properly, machine learning can deliver a double-digit improvement to most businesses.” Tushar Chandra, Google Distinguished Engineer © 2019 Google LLC. All rights reserved. Machine Learning Advanced Solutions Lab 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
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