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Democratizing AI/ML with GCP - Abishay Rao (Google) at GoDataFest 2019

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Every company today is talking about AI/ML, but when most companies talk about AI/ML in their transformation journey, you hear terms like Proof of Concept, Feasibility Study, Pilot, A/B Test. We are at the peak of AI's hype, but only 12% of enterprises have deployed AI in production. Google aims to make big data processing available for everyone, the possiblities of Big Query ML are endless: Marketing, retail, industrial and IoT, media, gaming, and so fort.

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Abishay Rao Partner Engineer - Google Cloud Democratizing AI/ML with GCP /abishayrao
Every company is talking about AI/ML today Source: AI Index Report 2018 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 0 50 100 150 Company earnings calls mentions - Sum of other industries ”Cloud”“Big data”“Artificial intelligence” “Machine Learning” Annualmentions 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 250 750 500 1,000 1,200 0 Annualmentions Company earnings calls mentions - IT companies ”Cloud”“Big data”“Artificial intelligence” “Machine Learning”
When most companies talk about AI/ML in their transformation journey, you hear terms like… PROOF OF CONCEPT PILOT A/B TEST FEASIBILITY STUDY KNOW DATA PROCEDURE BUSINESS ANALYSIS THESIS EXPERIMENT TEST CRITICAL PROOF CREATIVE REPORT PROCESS DEBATE REVIEW CORE MIX PROJECT VALUE INNOVATION PROBE BOOK STATISTICS CONCEPT CHECK SUMMARY SEARCH CHECK SUMMARY ANALYZING TEST VALUE BOOK BUSINESS INVESTIGATION EXPERIMENTS CHECK INVESTIGATE TEST DATA MIX PROCEDURES EFFECTIVE SOLUTIONS DATA BOOK ASSESS NEW PROOF EVIDENCE CONTINUAL QUANTITATIVE RESULTS ACADEMICS TRIAL SAMPLE RESULTS STUDIES MIX EXPLORE DISCOVERY PRELIMINARY DATA CONCEPT INSPECTION EXPLORATION PROBE VALUE VERIFICATIONEVENTS STUDIES REVIEWING FUNCTIONCASE FUNCTIONIONAL RESPONSIBILITY QUALITATIVE RESULTS MIX YES CONFIRMATION DOCUMENTATION CONFIRM NEW
We are at the peak of AI’s hype Research Reality ~90 AI papers per day 12% Of enterprises have deployed AI in production1 1 McKinsey Global Institute, Artificial Intelligence: The Next Digital Frontier 40000 30000 20000 10000 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 0 5 10 15 20 25 30 35 MLArxivPapers Relativeto2009MLArxivPapers ML Arxiv Papers Moore’s Law growth rate (2x/2 years) Year
Decision trees Engineers applied decision trees to drive machine outcomes. 70’s 90’s Modeling and simulation Faster computers and software paved the way to apply statistics to drive superior outcomes. Deep Learning Deep Learning ushered the possibility to solve previously unsolvable problems. 10’s Embedded Intelligence Businesses view AI as an integral part of product development and operational efficiency. Now AI will be synonymous with software
2019. Empower every industry to transform their business with AI 10,000X of deep learning researchers 2M ML experts 100X M business users 23M developers Builders
How can I make faster impact on business How do I spend less time preparing data How do I get my models to production faster and manage its lifecycle How do I build and deploy my models flexibly (on-premises, GCP) How do I collaborate with all users
GCP ML Stack Notebooks Data Services Models APIs KF Pipelines VM Images Reference Architectures Storage Compute Preprocessing Managed Services Deep Learning Virtual Machines AI Platform Training & Prediction Tooling/ SDKs Notebook On Prem infra Kubeflow Core Services DIY AutoML Kubeflow Fairing (Hybrid SDK) Kubeflow Pipelines GCP Infra Options BigQuery ML AI Hub + K8 + GKE Educational Materials BETA ... Open Source Frameworks
Visualize Accelerate your ML development with our unified, open and fully-managed architecture Endpoint clients User & device data Or Or Ingest Transform Analyze Web IoT Mobile PubSub Apache Kafka Apache Beam Dataflow Apache Spark BigQuery AI Platform Kubeflow Data Studio 3rd-party BI Tools Data consumers Or
Data warehouses From 1st-gen EDWs, increased data collection and analysis has helped build more data-driven businesses. 90’s 00’s BI foundations Data warehousing formed the foundation of reporting and business intelligence. Cloud data warehousing BigQuery represents a fundamentally different approach to cloud data warehousing. Now AI foundations We’re working to make BigQuery the foundation for organizations that will leverage machine intelligence in their businesses. Next Data warehousing for AI-driven business
Google BigQuery forms the AI foundation Automate data delivery Democratize data insights Build the foundation for AI Break data silos, power apps, add read-only data sets & make query results accessible to anyone Automated data transfer to extract data from your systems & shared data with federated querying across any Google service Enterprise Data Warehouse stores the most valuable data for your company & brings AI capabilities without replicating data into storage Tee up real-time insights Analyze real-time business events by automatic data ingestion, which is immediately available to query in your data warehouse
Google BigQuery Petabyte-scale storage and queries Encrypted, durable and highly available Real-time analytics on streaming data Google Cloud Platform’s enterprise data warehouse for analytics Convenience of standard SQL Fully managed and serverless
Days to months to create an ML model TensorFlow or scikit-learn Only an expert data scientist can do this Export small amounts of data from BQ Create frames of data for use with TensorFlow Build model Go back and get more data to create features, and improve performance Repeat. It’s hard, so you stop after a few iterations 3 Export data 1 Regression in Excel/Sheets Export small amounts of data from BQ Run linear regression Get a model with low accuracy due to small data for training Go back and get more data to create new features, and improve performance Repeat. It’s hard, so you stop after a few iterations 2
1 2 3 Execute ML initiatives without moving data from BigQuery Iterate on models in SQL in BigQuery to increase development speed Automate common ML tasks and hyperparameter tuning BigQuery ML
Through two lines of SQL ● Leverage BigQuery’s processing power to build a model ● Auto-tuned learning rate ● Auto-split of data into training and test ● Null imputation ● Standardization of numeric features ● One-hot encoding of strings ● Class imbalance handling Behind the scenes
Making ML accessible for all audiences Developer Data Analyst Data Scientist Use cases and skills TensorFlow and CloudML Engine ● Build and deploy state-of-art custom models ● Requires deep understanding of ML and programming BigQuery ML ● Build and deploy custom models using SQL ● Requires only basic understanding of ML AutoML and CloudML APIs ● Build and deploy Google-provided models for standard use cases ● Requires almost no ML knowledge
● StandardSQL and UDFs within the ML queries ● Linear regression (forecasting) ● Multi-nomial logistic regression (classification) ● Kmeans clustering (segmentation) ● Model evaluation functions for standard metrics, including the ROC curve ● Model weight inspection ● Feature distribution analysis through standard functions Supported features
Model evaluation on BigQuery UI
Available through your favorite BI Platform
Looker integration with BigQuery ML Explore data Create BigQuery table for model creation Create the model Evaluate model using a standard dashboard Operationalize the ML workflow Easily JOIN predictions into existing dashboards Alerts and scheduling
Retail Optimize inventory Forecast revenue Enable product recommendations Optimize staff promotions Marketing Predict customer lifetime value Predict funnel conversion Personalize ads, email, webpage content Industrial and IoT Forecast demand for parking, traffic, utilities, personnel Predict maintenance needs Prevent equipment downtime Media / gaming Personalize content Predict game difficulty Predict player lifetime value The possibilities are endless
22 BQML DEMO!
23 Thank you! Abishay Rao Partner Engineer - Google Cloud /abishayrao

More Related Content

Democratizing AI/ML with GCP - Abishay Rao (Google) at GoDataFest 2019

  • 1. Abishay Rao Partner Engineer - Google Cloud Democratizing AI/ML with GCP /abishayrao
  • 2. Every company is talking about AI/ML today Source: AI Index Report 2018 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 0 50 100 150 Company earnings calls mentions - Sum of other industries ”Cloud”“Big data”“Artificial intelligence” “Machine Learning” Annualmentions 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 250 750 500 1,000 1,200 0 Annualmentions Company earnings calls mentions - IT companies ”Cloud”“Big data”“Artificial intelligence” “Machine Learning”
  • 3. When most companies talk about AI/ML in their transformation journey, you hear terms like… PROOF OF CONCEPT PILOT A/B TEST FEASIBILITY STUDY KNOW DATA PROCEDURE BUSINESS ANALYSIS THESIS EXPERIMENT TEST CRITICAL PROOF CREATIVE REPORT PROCESS DEBATE REVIEW CORE MIX PROJECT VALUE INNOVATION PROBE BOOK STATISTICS CONCEPT CHECK SUMMARY SEARCH CHECK SUMMARY ANALYZING TEST VALUE BOOK BUSINESS INVESTIGATION EXPERIMENTS CHECK INVESTIGATE TEST DATA MIX PROCEDURES EFFECTIVE SOLUTIONS DATA BOOK ASSESS NEW PROOF EVIDENCE CONTINUAL QUANTITATIVE RESULTS ACADEMICS TRIAL SAMPLE RESULTS STUDIES MIX EXPLORE DISCOVERY PRELIMINARY DATA CONCEPT INSPECTION EXPLORATION PROBE VALUE VERIFICATIONEVENTS STUDIES REVIEWING FUNCTIONCASE FUNCTIONIONAL RESPONSIBILITY QUALITATIVE RESULTS MIX YES CONFIRMATION DOCUMENTATION CONFIRM NEW
  • 4. We are at the peak of AI’s hype Research Reality ~90 AI papers per day 12% Of enterprises have deployed AI in production1 1 McKinsey Global Institute, Artificial Intelligence: The Next Digital Frontier 40000 30000 20000 10000 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 0 5 10 15 20 25 30 35 MLArxivPapers Relativeto2009MLArxivPapers ML Arxiv Papers Moore’s Law growth rate (2x/2 years) Year
  • 5. Decision trees Engineers applied decision trees to drive machine outcomes. 70’s 90’s Modeling and simulation Faster computers and software paved the way to apply statistics to drive superior outcomes. Deep Learning Deep Learning ushered the possibility to solve previously unsolvable problems. 10’s Embedded Intelligence Businesses view AI as an integral part of product development and operational efficiency. Now AI will be synonymous with software
  • 6. 2019. Empower every industry to transform their business with AI 10,000X of deep learning researchers 2M ML experts 100X M business users 23M developers Builders
  • 7. How can I make faster impact on business How do I spend less time preparing data How do I get my models to production faster and manage its lifecycle How do I build and deploy my models flexibly (on-premises, GCP) How do I collaborate with all users
  • 8. GCP ML Stack Notebooks Data Services Models APIs KF Pipelines VM Images Reference Architectures Storage Compute Preprocessing Managed Services Deep Learning Virtual Machines AI Platform Training & Prediction Tooling/ SDKs Notebook On Prem infra Kubeflow Core Services DIY AutoML Kubeflow Fairing (Hybrid SDK) Kubeflow Pipelines GCP Infra Options BigQuery ML AI Hub + K8 + GKE Educational Materials BETA ... Open Source Frameworks
  • 9. Visualize Accelerate your ML development with our unified, open and fully-managed architecture Endpoint clients User & device data Or Or Ingest Transform Analyze Web IoT Mobile PubSub Apache Kafka Apache Beam Dataflow Apache Spark BigQuery AI Platform Kubeflow Data Studio 3rd-party BI Tools Data consumers Or
  • 10. Data warehouses From 1st-gen EDWs, increased data collection and analysis has helped build more data-driven businesses. 90’s 00’s BI foundations Data warehousing formed the foundation of reporting and business intelligence. Cloud data warehousing BigQuery represents a fundamentally different approach to cloud data warehousing. Now AI foundations We’re working to make BigQuery the foundation for organizations that will leverage machine intelligence in their businesses. Next Data warehousing for AI-driven business
  • 11. Google BigQuery forms the AI foundation Automate data delivery Democratize data insights Build the foundation for AI Break data silos, power apps, add read-only data sets & make query results accessible to anyone Automated data transfer to extract data from your systems & shared data with federated querying across any Google service Enterprise Data Warehouse stores the most valuable data for your company & brings AI capabilities without replicating data into storage Tee up real-time insights Analyze real-time business events by automatic data ingestion, which is immediately available to query in your data warehouse
  • 12. Google BigQuery Petabyte-scale storage and queries Encrypted, durable and highly available Real-time analytics on streaming data Google Cloud Platform’s enterprise data warehouse for analytics Convenience of standard SQL Fully managed and serverless
  • 13. Days to months to create an ML model TensorFlow or scikit-learn Only an expert data scientist can do this Export small amounts of data from BQ Create frames of data for use with TensorFlow Build model Go back and get more data to create features, and improve performance Repeat. It’s hard, so you stop after a few iterations 3 Export data 1 Regression in Excel/Sheets Export small amounts of data from BQ Run linear regression Get a model with low accuracy due to small data for training Go back and get more data to create new features, and improve performance Repeat. It’s hard, so you stop after a few iterations 2
  • 14. 1 2 3 Execute ML initiatives without moving data from BigQuery Iterate on models in SQL in BigQuery to increase development speed Automate common ML tasks and hyperparameter tuning BigQuery ML
  • 15. Through two lines of SQL ● Leverage BigQuery’s processing power to build a model ● Auto-tuned learning rate ● Auto-split of data into training and test ● Null imputation ● Standardization of numeric features ● One-hot encoding of strings ● Class imbalance handling Behind the scenes
  • 16. Making ML accessible for all audiences Developer Data Analyst Data Scientist Use cases and skills TensorFlow and CloudML Engine ● Build and deploy state-of-art custom models ● Requires deep understanding of ML and programming BigQuery ML ● Build and deploy custom models using SQL ● Requires only basic understanding of ML AutoML and CloudML APIs ● Build and deploy Google-provided models for standard use cases ● Requires almost no ML knowledge
  • 17. ● StandardSQL and UDFs within the ML queries ● Linear regression (forecasting) ● Multi-nomial logistic regression (classification) ● Kmeans clustering (segmentation) ● Model evaluation functions for standard metrics, including the ROC curve ● Model weight inspection ● Feature distribution analysis through standard functions Supported features
  • 19. Available through your favorite BI Platform
  • 20. Looker integration with BigQuery ML Explore data Create BigQuery table for model creation Create the model Evaluate model using a standard dashboard Operationalize the ML workflow Easily JOIN predictions into existing dashboards Alerts and scheduling
  • 21. Retail Optimize inventory Forecast revenue Enable product recommendations Optimize staff promotions Marketing Predict customer lifetime value Predict funnel conversion Personalize ads, email, webpage content Industrial and IoT Forecast demand for parking, traffic, utilities, personnel Predict maintenance needs Prevent equipment downtime Media / gaming Personalize content Predict game difficulty Predict player lifetime value The possibilities are endless
  • 23. 23 Thank you! Abishay Rao Partner Engineer - Google Cloud /abishayrao