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Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramsey (Google US)

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Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramsey (Google US)

  1. 1. Solving Business Problems with Machine Learning Google Analytics Conference Vienna April 19 2018
  2. 2. Lukman Ramsey lramsey@google.com Head of AI Solutions Google Cloud
  3. 3. Democratizing AI and Machine Learning Three flavors of Machine Learning Machine Learning APIs Building custom ML solutions on Google Cloud Customer Success Stories Agenda
  4. 4. © 2017 Google Inc. All rights reserved.© 2018 Google LLC. All rights reserved. Democratizing AI and ML
  5. 5. AI?Artificial Intelligence Machine Learning is...One branch of the field of Artificial Intelligence
  6. 6. Underneath it all is machine learning data algorithmscomputations
  7. 7. Two Models Of Computation Turing Machines Von-Neumann Architecture Algorithms Programmed by Humans Symbolic Vector Space Brain Machines Biologically Inspired (Evolutionarily Evolved) Architecture Algorithms Learned from Experience
  8. 8. Confidential & Proprietary Source: Data scientists= Kaggle Data scientist community , Developers: Evans Data Corporation the figure in 2016 was 21m State of the Industry: Lack of Expertise Very few users today can create a custom ML model. To democratize AI, we need to make AI accessible to millions more 1000’s Deep Learning Researchers 21M Developers Confidential & Proprietary <1M Data Scientists
  9. 9. Confidential & ProprietaryConfidential & Proprietary UPDATEDEPLOYEVALUATETUNE ML MODEL PARAMETERS ML MODEL DESIGN DATA PREPROCESSING State of the Industry: Complex & Time Intensive Large computational resource . Machine learning expertise . Manual data labeling
  10. 10. Google is an AI company
  11. 11. Confidential & ProprietaryGoogle Cloud Platform 11 Rapidly accelerating use of deep learning at Google Google Cloud Platform 11 Google directories containing Brain Models 2012 2013 2014 2015 3000 2000 1000 0 Used across products: 4000 2016 Uniqueprojectdirectories 2017
  12. 12. Confidential & Proprietary Democratize AI by making it accessible, fast and useful for enterprises and developers
  13. 13. © 2017 Google Inc. All rights reserved.© 2018 Google LLC. All rights reserved. Three Flavors of Machine Learning
  14. 14. Proprietary + Confidential ML Frameworks: Total Control for Power Users Machine Learning APIs: Ready to Go Cloud Vision API Cloud Translation API Cloud Natural Language API Cloud Speech API Cloud ML EngineTensorFlow Cloud Jobs API Cloud Video Intelligence API AutoML: Bring Your Own Data (We Do the Rest) Pick Your Flavor Cloud DataprocSpark ML
  15. 15. Proprietary + Confidential Ease of Use vs Flexibility
  16. 16. Proprietary + Confidential ML Frameworks: Total Control for Power Users Machine Learning APIs: Ready to Go Cloud Vision API Cloud Translation API Cloud Natural Language API Cloud Speech API Cloud ML EngineTensorFlow Cloud Jobs API Cloud Video Intelligence API AutoML: Bring Your Own Data (We Do the Rest) Pick Your Flavor Cloud DataprocSpark ML
  17. 17. ML APIs
  18. 18. Vision API Detect broad sets of categories within an image, ranging from modes of transportation to animals. Analyze facial features to detect emotions: joy, sorrow, anger. Detect logos. Detect and extract text within an image, with support for a broad range of languages, along with support for automatic language identification. Extract text Detect different types of inappropriate content from adult to violent content. Powered by Google Safe Search Detect inappropriate contentObject Recognition Facial sentiment & logos TRY THE API
  19. 19. Natural Language API Identify entities and label by types such as person, organization, location, events, products and media. Enables you to easily analyze text in multiple languages including English, Spanish and Japanese. Extract tokens and sentences, identify parts of speech (PoS) and create dependency parse trees for each sentence. Syntax analysisEntity Recognition Multi-Language Support TRY THE API Understand the overall sentiment expressed in a block of text. Sentiment Analysis
  20. 20. Speech API Powered by deep learning neural networking to power your applications.. No need for signal processing or noise cancellation before calling API. Can handle noisy audio from a variety of environments. Noise Robustness Can provide context hints for improved accuracy. Especially useful for device and app use cases. Word HintsSpeech Recognition TRY THE API Recognizes over 80 languages & variants. Can also filter inappropriate content in text results Over 80 languages Can stream text results, returning partial recognition results as they become available. Can also be run on buffered or archived audio files. Real-time results
  21. 21. Translation API Supports more than 100 languages and thousands of language pairs. Behind the scenes, Translation API is learning from logs analysis and human translation examples. Existing language pairs improve and new language pairs come online at no additional cost. Sometimes you don’t know your source text language in advance. Can automatically identify languages with high accuracy. Automatic language detection The Premium edition is tailored for users who need precise, long-form translation services (e.g. livestream translations, high volume of emails, detailed articles and documents) Premium edition BETA Text Translation Continuous Updates TRY THE API
  22. 22. Video Intelligence API Detect entities within the video, such as "dog", "flower" or "car". You can now search your video catalog the same way you search text documents.. Extract actionable insights from video files without requiring any machine learning or computer vision knowledge. Enable Video Search More features will be added to the Video Intelligence API during the BETA period. More to come ... BETA Label Detection Insights From Videos
  23. 23. @glaforge @manekinekko Cross-platform tool for building advanced conversational interfaces ListSessions( topics.ComputeEngine); “What Compute Engine talks are there?”
  24. 24. @glaforge @manekinekko Integrate with... Actions on Google ● Google Home, Pixel… ● and more to come External integrations ● Slack, Facebook Messenger, ● Twitter, Twilio, Skype, Tropo, ● Telegram, Kik, LINE, Cisco Spark, ● Alexa, Cortana
  25. 25. Demo: Vision API
  26. 26. Demo: Natural Language API
  27. 27. Proprietary + Confidential ML Frameworks: Total Control for Power Users Machine Learning APIs: Ready to Go Cloud Vision API Cloud Translation API Cloud Natural Language API Cloud Speech API Cloud ML EngineTensorFlow Cloud Jobs API Cloud Video Intelligence API AutoML: Bring Your Own Data (We Do the Rest) Pick Your Flavor Cloud DataprocSpark ML
  28. 28. AutoML
  29. 29. Confidential & ProprietaryGoogle Cloud Platform 29 AI expertise + Data + Computation We call this AutoML Current solution But we can turn this into Data + 100x Computation
  30. 30. How does AutoML work? Controller: proposes ML models Train & evaluate models 20K times Iterate to find the most accurate model
  31. 31. Confidential & ProprietaryGoogle Cloud Platform 31 AI does AI Systematic exploration of the model space, using the techniques finessed in AlphaGo, yields super-human performance in AI network design
  32. 32. CIFAR-10 Image Recognition Task
  33. 33. AutoML for Cloud Customers Dataset Baseline AutoML Customer 1 (Media) 75% 99% Customer 2 (Housing) 87% 93% Customer 3 (Wildlife) 85% 95% Customer 4 (Sports) 90% 96% Customer 5 (Insurance) 0.7 mean AUC 0.95 mean AUC Results for AutoML on image problems https://cloud.google.com/automl/
  34. 34. Proprietary + Confidential ML Frameworks: Total Control for Power Users Machine Learning APIs: Ready to Go Cloud Vision API Cloud Translation API Cloud Natural Language API Cloud Speech API Cloud ML EngineTensorFlow Cloud Jobs API Cloud Video Intelligence API AutoML: Bring Your Own Data (We Do the Rest) Pick Your Flavor Cloud DataprocSpark ML
  35. 35. Core technology for ML
  36. 36. ● PaaS for Tensorflow ● Instantly scale your training up to 100 workers (industry leading) ● Automatic monitoring and logging ● Seamlessly transition from training to prediction ● Built in model version management ● No lock-in. Option to download your trained models for on-premise or mobile deployment Cloud ML Engine
  37. 37. 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 ● Flexible: Hyperparameters are provided to to user code as command line flags, allowing any post-processing you want. HyperParam #1 Objective Want to find this Not these HyperParam #2
  38. 38. TensorFlow
  39. 39. Google Use Of TensorFlow: # of Models Search Gmail Translate Maps Android Photos Speech YouTube Play … many others ... Production use in many areas: Research use for: 100s of projects and papers Internal TensorFlow launch
  40. 40. Google-designed custom ASIC built and optimized for TensorFlow 1st generation used in production for over 16 months Now on 2nd generation—180 Teraflops per TPU TensorFlow Research Cloud—1000 TPUs for researchers, at no charge. Tensor Processing Unit
  41. 41. TPU Pod 64 2nd-gen TPUs 11.5 petaflops 4 terabytes of memory 2-D toroidal mesh network
  42. 42. © 2017 Google Inc. All rights reserved.© 2018 Google LLC. All rights reserved. Building custom ML solutions on Google Cloud
  43. 43. Proprietary + Confidential Define ML use cases Define specific ML use cases for the project Select algorithm Choose the right ML algorithm for the task Build ML model Develop the first iteration of the ML model Present results Present results of the model in a way that demonstrates its value to stakeholders Iterate ML model Refine the ML model to improve performance and efficacy Data pipeline & feature engineering Create the right features from raw data for the ML task Plan for deployment Prepare for deployment in production Operationalize model Deploy and operationalize ML model in production Monitor model Monitor deployed ML model and retrain or rebuild when performance degrades 1 3 10 789 Data exploration Perform exploratory data analysis to understand the data 2 4 6 5 Start a new ML project with PSO Cloud Discover Cloud MVM Cloud Deploy Machine Learning Lifecycle
  44. 44. Building an ML model requires 3 things Data Compute Talent Data Scientist Software Engineer
  45. 45. Data Data Compute Talent Data Scientist Software Engineer
  46. 46. It's not who has the best algorithm who wins, it's who has the most data. — Andrew Ng , Co-Founder of Google Brain “ ”
  47. 47. Capture Pub/Sub Process Dataflow Dataproc Store Cloud Storage BigQuery Cloud SQL Datastore BigTable Analyze BigQuery Dataflow Datalab ML starts with getting a handle on your data Insight Cloud ML Engine
  48. 48. ● Easily access and analyze public and commercial datasets hosted on GCP Commercial Datasets Program
  49. 49. Compute Data Compute Talent Data Scientist Software Engineer
  50. 50. Talent Data Compute Talent Data Scientist Software Engineer
  51. 51. Talent ● Every organization has people capable of building ML systems ● But those people may not have the training and tools they need to be successful with machine learning ● Google provides both
  52. 52. Training ● Google Professional Services will bring Google Machine Learning expertise to your company ● Intensive trainings and workshops from 1 to 4 weeks ● Customized to your needs Talent
  53. 53. Proprietary + Confidential We Can Help You Implement your Solution Google PSO1 Google + Partner2 3 Implement Machine Learning APIs Build ML Models Cloud ML Engine Deploy and ManageAnalyze and Plan Google Cloud Google Cloud
  54. 54. Proprietary + Confidential Define ML use cases Define specific ML use cases for the project Select algorithm Choose the right ML algorithm for the task Build ML model Develop the first iteration of the ML model Present results Present results of the model in a way that demonstrates its value to stakeholders Iterate ML model Refine the ML model to improve performance and efficacy Data pipeline & feature engineering Create the right features from raw data for the ML task Plan for deployment Prepare for deployment in production Operationalize model Deploy and operationalize ML model in production Monitor model Monitor deployed ML model and retrain or rebuild when performance degrades 1 3 10 789 Data exploration Perform exploratory data analysis to understand the data 2 4 6 5 Start a new ML project with PSO Cloud Discover Cloud MVM Cloud Deploy Machine Learning Lifecycle
  55. 55. © 2018 Google LLC. All rights reserved. 2-day workshop, up to 5 days Objectives ● Gain new competitive advantages with ML ● Identify potential ML use cases ● Address targeted business problems ● Survey the foundation for ML potential Activities and Deliverables 1. ML overview session 2. Use case ideation workshop 3. High-level data qualification 4. Analysis and recommendations Cloud Discover: Machine Learning Cloud Discover: Machine Learning helps you understand machine learning (ML) concepts and identify and qualify potential business problems that can be addressed using ML. Ideal for determining if ML is right for your business and which use cases are realistic and achievable.
  56. 56. © 2018 Google LLC. All rights reserved. 2+ months Engagement activities ● Develop an ML solution model ● Perform exploratory data analysis ● Explore the right set of features to include ● Present results of the model that shows its business value Deliverables 1. Data exploration 2. Data pipeline and feature engineering 3. Build and iterate ML model 4. Present results to stakeholders Cloud Deploy ML for MVM Cloud Deploy ML for MVM (minimum viable model) helps you take the use case identified in Cloud Discover from theoretical to practical by developing an ML model. This model will prove the value of the use case and its ML solution to stakeholders prior to investing more in the next phase of build out.
  57. 57. © 2018 Google LLC. All rights reserved. Cloud Deploy ML for MVM details Implements steps 2–7 in the ML lifecycle Delivers a minimum viable ML model or feasibility study Delivers 32 days of work effort in a 2-month calendar window Prices start at $160K for 1 SCE at 100% and 1 consultant at 50%
  58. 58. © 2018 Google LLC. All rights reserved. ● Data exploration ● Algorithm selection ● Data pipeline ● Feature engineering ● Development of initial ML model ● Iteration to improve performance of ML model ● Building a complete data pipeline ● Deploying the model into production ● Converting the model into an API In scope: Out of scope: Cloud Deploy ML for MVM scope
  59. 59. © 2018 Google LLC. All rights reserved. How do we deploy? (detailed level) 32-day work effort (minimum) Advisory on ML model deployment Prerequisite: Discover ML Typical machine learning journey Assess Accelerate Transform Cloud Discover Assessment What do we? Can we? 7-day FTE work effort ML concepts training Group ideation with business and data owners of use cases Use case technical qualification Next steps on how to create a test model for the use cases • • • • • • Step 1 should be taken to determine a feasible ML use case. Qualified customers can proceed directly to Step 3 unless they need training or are not fully committed to starting a formal ML project. Data Engineering /GCP class What is ML? How to use data? 4-day training w/ hands-on lab • • ASL: Immersion Education How to code ML? 1-month immersive training at Google using public datasets • • Cloud Deploy ML for MVM • • • • ASL: Solution Development Let’s build an ML model together 6-month solution development w/Google engineers for one use case Prerequisite: qualified use case • • • Cloud Deploy ML for Production How do we deploy? (detailed level) 20-day work effort (minimum) Advisory on ML model deployment Prerequisite: ML MVM • • • •
  60. 60. Packaged Solutions
  61. 61. Confidential & Proprietary Production Recommendation Solution on GCP Google Analytics BigQuery Google Analytics 360 Customer Web Application Web Server Application Server Database Server Rec API App Engine Cloud Endpoints Model Training Cloud Machine Learning Orchestration Cloud Composer ML Data Training Model files Browser Client Mobile / Tablet Client
  62. 62. Confidential & Proprietary Kurier.at 3rd Largest news provider in Austria Parent company: 500 web site properties Google Analytics 360 customer Outbrain user… Not happy eDialog GA Partner
  63. 63. © 2017 Google Inc. All rights reserved.© 2018 Google LLC. All rights reserved. Customer Success Stories
  64. 64. Google Cloud Customers: Data & Machine Learning
  65. 65. © 2018 Google LLC. All rights reserved. • Risk analytics and regulation • Fraud detection • Credit worthiness evaluation • Customer segmentation • Cross-selling and upselling • Sales and marketing campaign management Financial Services • Aircraft scheduling • Dynamic pricing • Social media – consumer feedback and interaction analysis • Customer complaint resolution • Traffic patterns and congestion management Travel and Hospitality Explore machine learning use cases with Google • 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 stratification • Patient triage optimization • Proactive health management • Healthcare provider sentiment analysis Healthcare and Life Sciences • Predictive maintenance or condition monitoring • Warranty reserve estimation • Propensity to buy • Demand forecasting • Process optimization • Telematics Manufacturing • 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
  66. 66. complex solve The ability to detect patterns in satellite images — such as the difference between snow and clouds —​is critical to Airbus Defense and Space’s users who depend on highly precise, up-to-date and reliable information. Produits Utilisés Google Cloud Dataflow, BigQuery, Cloud Storage et Cloud Datalab Industry: Aerospace – Region: France “In our tests, Google Cloud Machine Learning enabled us to improve the accuracy and speed at which we analyze the images captured from our satellites. It solved a problem that has existed for decades" . Mathias Ortner, Data Analysis and Image Processing Lead Solutions One of our customers, Airbus Defense and Space, tested the use of Google Cloud Machine Learning to automate the process of detecting and correcting satellite images that contain imperfections such as the presence of cloud formations. problems opportunities identify with advancements such as Machine Learning Speed at which we analyze the images captured Accuracy improved thanks to Machine Learning
  67. 67. Gain Insights from images by detecting individual objects and concepts Intelligent structure for 30 billion files managed with powerful capabilities Speed up search and discovery with image-centric workflows Enabled accuracy for automation performance Industry: Technology – Region: North America Box customers unlock new value from content as they automatically classify images and greatly accelerate business processes. Improved Extensive content management for customers in every industry Products Used Google Cloud Vision API Solution Google Cloud Platform and machine learning enable Box to help its customers manage and gain insight from their image files, and speed up image-centric processes and workflows.
  68. 68. 119languages and dialects supported Machine learning that Enables applications that react to what people say Understand intent of callers in addition to what they say Cutting-edge speech recognition capabilities Products Used Google Cloud Speech API Industry: Technology – Region: North America “Machine Learning has changed the game.” Jeff Lawson, CEO, Twilio Expanded support in Twilio Understand with Google Cloud Speech API Solution Google Speech API enables Twilio to make it easier for developers to build applications that react to what people say during phone calls, taking callers speech and turn it to text. By adding a layer of machine intelligence over existing support, customers can bypass navigating menus and using phone keypads.
  69. 69. Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything. - Sundar Pichai “ ”
  70. 70. Thank you lramsey@google.com Proprietary + Confidential

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