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Google Cloud Machine Learning

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Google Cloud Machine Learning

  1. 1. Cloud Machine Learning Google Cloud Platform Business Development ) (Customer Engineer)
  2. 2. Data is exploding. And smart companies are taking advantage.
  3. 3. Unstructured data accounts for 90% of enterprise data* Cloud Machine Learning help you make sense of it *Source: IDC
  4. 4. Confidential & ProprietaryGoogle Cloud Platform 4 What is Machine Learning? Data Algorithm Insight
  5. 5. Confidential & ProprietaryGoogle Cloud Platform 5 Machine Learning @ Google
  6. 6. Beach Woman Pool Coast Water
  7. 7. Confidential & ProprietaryGoogle Cloud Platform 7 Google Translate
  8. 8. Confidential & ProprietaryGoogle Cloud Platform 8
  9. 9. Confidential & ProprietaryGoogle Cloud Platform 9 Enterprise Predictive Analytics Challenges Data access to a variety of data sources. Develop and build analytic models. Data preparation, exploration and visualization. Deploy models and integrate them into business processes and applications. High performance and scalability for both development and deployment. Perform platform, project and model management.
  10. 10. Confidential & ProprietaryGoogle Cloud Platform 10 Data Warehouse is the Foundation of Something Bigger Data Warehouses/Lakes Machine Intelligence Predictive + Prescriptive Analytics = Advanced Analytics Cloud On Premises Machine Learning APIs Train Your Own Models
  11. 11. Confidential & ProprietaryGoogle Cloud Platform 11 Machine Learning Use Cases • Predictive maintenance or condition monitoring • Warranty reserve estimation • Propensity to buy • Demand forecasting • Process optimization Manufacturing • 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 satisfaction • Patient triage optimization • Proactive health management • Healthcare provider sentiment analysis Healthcare and Life Sciences • Aircraft scheduling • Dynamic pricing • Social media – consumer feedback and interaction analysis • Customer complaint resolution • Traffic patterns and congestion management Travel and Hospitality • Risk analytics and regulation • Customer Segmentation • Cross-selling and up-selling • Sales and marketing campaign management • Credit worthiness evaluation Financial Services • 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
  12. 12. Confidential & ProprietaryGoogle Cloud Platform 12 Why So Little Machine Learning Apps Out There? • Building and scaling machine learning infrastructure is hard • Operating production ML system is time consuming and expensive
  13. 13. Confidential & ProprietaryGoogle Cloud Platform 13 Building Smart Applications Today Technology Operationalization Tooling Difficult to scale Many choices for different use cases Using latest technology (e.g. DNN) is hard Complex data pipelines Managing ML infra takes away time from actually doing ML Many models to manage Complex dev pipeline with many combinations of tools/libraries Not fully interactive developer experience - collaboration/sharing is hard
  14. 14. Confidential & ProprietaryGoogle Cloud Platform 14 Introducing Cloud Machine Learning ● Fully managed service ● Train using a custom TensorFlow graph for any ML use cases ● Training at scale to shorten dev cycle ● Automatically maximize predictive accuracy with HyperTune ● Batch and online predictions, at scale ● Integrated Datalab experience
  15. 15. Confidential & ProprietaryGoogle Cloud Platform 15 Cloud Datalab ● Interactively explore data ● Define features with rich visualization support ● Launch training and evaluation ● ML lifecycle support ● Combine code, results, visualizations & documentation in notebook format ● Share results with your team ● Pick from a rich set of tutorials & samples to learn and get started with your project
  16. 16. Confidential & ProprietaryGoogle Cloud Platform 16 Powerful Machine Learning Algorithm ● Convolutional Neural Network for image classification ● Recursive Neural network for text sentiment analysis ● Linear regression at scale to predict consumer action (purchase prediction, churn analysis) ● And unlimited variety of algorithms you can build using TensorFlow
  17. 17. Confidential & ProprietaryGoogle Cloud Platform 17 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 HyperParam #1 Objective Want to find this Not these HyperParam #2
  18. 18. Confidential & ProprietaryGoogle Cloud Platform 18 Integrated with GCP Products ● Access data that is stored in GCS or BigQuery ● Save trained models to GCS ● Preprocess largest datasets (TB) using Dataflow ● Orchestrate ML workflow as a Dataflow pipeline ● Analyze data and interactively develop ML models in Datalab
  19. 19. Confidential & ProprietaryGoogle Cloud Platform 19 Fully Managed Machine Learning Services ● Scalable and distributed training infrastructure for your largest data sets ● Scalable prediction infrastructure that can serve very large traffic ● Managed no-ops infrastructure handles provisioning, scaling, and monitoring so that you can focus on building your models instead of handling clusters
  20. 20. Confidential & ProprietaryGoogle Cloud Platform 20 Pay As You Go and Inexpensive Tier Price Regular $0.1 / 1K +$0.40/Node Hour Large volume $0.05/1K +$0.40/Node Hour after 100M/month Training Prediction US Europe / Asia 1 ML training unit $0.49 $0.54 Tier Training unit per hour Characteristics BASIC 1 A single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets. STANDARD _1 10 Mid-size cluster with many workers and a few parameter servers for medium scale distributed training PREMIUM_ 1 75 Larger cluster with a large number of workers with many parameter servers. Suitable for large scale job with complex and larger models CUSTOM Custom Fine tune the number of workers, parameter servers and machine types
  21. 21. Do You Have The Right Visibility? ** Ventura Research Report** Ventura Research Report 34% 51% 71% Of retail companies are satisfied with the processes they use to create analytics. Of retailers are still using spreadsheets as their primary data analysis tools Find challenge in data sharing 45% Are not effectively using data to personalize marketing communications 42% Are not able to link data together at the individual customer level Largest ObstacleRetail Analytic Trends
  22. 22. Challenges Difficulty Understanding Customers What drives the customers buying habits? What products do customers prefer to buy and what related products? What causes customers to not buy? Customizing The Experience How can I ensure each customer sees the products they’re interested in as quickly as possible? How can my eCommerce app react in real-time to customer actions? Data Aggregation & Processing Need for a large, scalable storage solution to aggregate, store, and serve applications Compute capacity required to churn and derive insights constantly increasing Analytics & Machine Learning can be resource hogs Key Takeaways Data is a core business asset Analytics drive competitive advantage Data at scale drives exponential complexity Traditional BI does not scale to big data Most organizations cannot capture all data Information growing faster than it can be leveraged
  23. 23. Retail Drivers - How Analytics Can Help? Demanding Customers Aggressive Competition Cost Optimization Improve Experience Understand Customers Faster Conversions Increase Sales Customer Profiling Segmentation Recommendations Cart Analysis Market Hot Spotting Asset Performance Social Media Analysis Customer Personalization Data Aggregation Multiple Platforms Location Planning Catchment Analysis Inventory Management Logistics Management Sales Forecasting Impact Analysis Risk Modeling
  24. 24. Confidential & ProprietaryGoogle Cloud Platform 24 Transform Data into Actions Exploration & Collaboration Databases Storage Data Preparation & Processing Analytics Advanced Analytics & Intelligence Mobile apps Sensors and devices Web apps Relational Key-value Document SQL Wide column Object Stream processing Batch processing Data preparation Federated query Data catalog Data exploration Data visualization Developers Data scientists Business analysts Development environment for Machine Learning Pre-Trained Machine Learning models Data Ingestion Messaging Logs
  25. 25. Confidential & ProprietaryGoogle Cloud Platform 25 Transform Data into Actions Data Preparation & Processing Cloud Dataflow Cloud Dataproc Exploration & Collaboration Google BigQuery Cloud Datalab Google Analytics 360 Cloud Dataproc Mobile apps Sensors and devices Web apps Developers Data scientists Business analysts Data Ingestion Cloud Pub/Sub App Engine Databases/ Storage Cloud SQL Cloud Bigtable Cloud Datastore Cloud Storage Analytics Google BigQuery Google Analytics 360 Cloud Dataproc Google Drive Advanced Analytics & Intelligence Cloud Machine Learning Translate API Vision API Speech API
  26. 26. Confidential & ProprietaryGoogle Cloud Platform 26 Use Your Own Data to Train Models BETA BETA GAGA Cloud Datalab Cloud Machine Learning Cloud Storage Google BigQuery Develop/Model/Test
  27. 27. Confidential & ProprietaryGoogle Cloud Platform 27 HTTP request Use your own data to train models Pre-ProcessingData Storage Training flow Prediction flow Local training Download Mobile prediction Batch Online Training Prediction Tooling Datalab Datalab Tooling Upload Hosted Model
  28. 28. Confidential & ProprietaryGoogle Cloud Platform 28 Automatically categorize, and automatically extract value Evaluate the model by applying it against additional manually categorized data, correct and tune Capture thousands of examples of correct evaluations for that categorization, and use them to train an ML model Identify categorizations that provide value, categories you’re already evaluating for by hand today 1 2 3 4 Machine Intelligence is Already Making a Huge Difference and There are Many, Many More Opportunities
  29. 29. Confidential & ProprietaryGoogle Cloud Platform 29 Machine Learning @ Google Level 200
  30. 30. Confidential & ProprietaryGoogle Cloud Platform 30 The point of ML is to make predictions Input Feature Predicted Value Model
  31. 31. Confidential & ProprietaryGoogle Cloud Platform 31 Tensorflow helps you “train” models Input Feature Predicted Value Model True Value Update model based on Cost Cost
  32. 32. Confidential & ProprietaryGoogle Cloud Platform 32 Democratizing machine learning App DeveloperData Scientist CloudML Build custom modelsUse/extend OSS SDK Scale, No-ops Infrastructure ML APIs Vision API Speech API Use pre-built models Translate API ML researcher Language API
  33. 33. Confidential & ProprietaryGoogle Cloud Platform 33 Beyond Tensorflow Size of dataset Size of NN Scale of Compute Problem Accuracy CloudML ( ) Deep networks TensorFlow Processing Units (TPUs) Distributed No-ops ML APIs Vision API Speech API Translate API Language API
  34. 34. Confidential & ProprietaryGoogle Cloud Platform 34 ML APIs are simply REST calls and can be made from any language or framework sservice = build('speech', 'v1beta1', developerKey=APIKEY) response = sservice.speech().syncrecognize( body={ 'config': { 'encoding': 'LINEAR16', 'sampleRate': 16000 }, 'audio': { 'uri': 'gs://cloud-training-demos/vision/audio.raw' } }).execute() print response Data on Cloud Storage
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