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AWS Customer Presentation - Angelbeat Princeton Seminar

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Build Smart Systems with AWS IOT, Analytics & AI

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AWS Customer Presentation - Angelbeat Princeton Seminar

  1. 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Neel Mitra Solutions Architect , AWS Sep 2017 Build Smart Systems with AWS IOT , Analytics & AI
  2. 2. A Flywheel For Data Machine Learning Deep Learning AI More Users Better Products More Data Better Analytics Object Storage Databases Data warehouse Streaming analytics BI Hadoop Spark/Presto Elasticsearch Click stream User activity Generated content Purchases Clicks Likes Sensor data
  3. 3. Three pillars of IoT Things Sense & Act Cloud Storage & Compute Intelligence Insights & Logic → Action AWS Greengrass
  4. 4. AWS IoT DEVICE SDK Set of client libraries to connect, authenticate and exchange messages DEVICE GATEWAY Communicate with devices via MQTT and HTTP AUTHENTICATION Secure with mutual authentication and encryption RULES ENGINE Transform messages based on rules and route to AWS Services AWS Services - - - - - 3P Services SHADOW Persistent thing state during intermittent connections APPLICATIONS AWS IoT API REGISTRY Identity and Management of your things
  5. 5. Most machine data never reaches the cloud Medical equipment Industrial machinery Extreme environments
  6. 6. Why this problem isn’t going away Law of physics Law of economics Law of the land
  7. 7. Things Sense & Act Cloud Storage & Compute Intelligence Insights & Logic → Action AWS IoT Starting in the cloud Action Device State AWS Services Applications Authentication & Authorization Device Gateway Registry AWS IoT API Messages Messages
  8. 8. Messages Messages Authentication & Authorization Device Gateway Action Device State AWS Services Applications Registry AWS IoT API AWS IoT Going to the edge Introducing AWS Greengrass Device State Action Device Gateway Messages Authentication & Authorization Security *Note: Greengrass is NOT Hardware (You bring your own)
  9. 9. Local Lambda Local Device Shadows Local Security Greengrass is … AWS Local Broker Greengrass is:
  10. 10. Device Patterns on AWS
  11. 11. Point to Point SUB: vacuum/10930Device Gateway Mobile App { “command”: “clean” } PUB: vacuum/10930 Start Cleaning
  12. 12. Broadcast Pattern SUB: cars/us_MA/weather Device Gateway Weather Service { “forecast”: “snow” “prob”: “85%” “geo”: [42.3,71.0] } PUB: cars/us_MA/weather Reduce Speed Ignore Reduce Speed Connected Vehicle: https://www.youtube.com/watch?v=o1cN0KDaOf4
  13. 13. Fan Out Notification Pattern Device Gateway Repair Service { “rep-102”: “shipped” } PUB: SN/{serial}/repair { “rep-111”: “delayed” } Alert Arrival Day Change Gear Speed SUB: SN/SN-2390/repair SUB: SN/SN-2289/repair PUB: SN/SN-2390/repair PUB: SN/SN-2289/repair
  14. 14. Aggregator Pattern PUB: cleaning/vac-1203/home1234 PUB: cleaning/vac-1204/home1234 Device Gateway SUB: cleaning/+/home1234 { “vac-1203”: “cleaning” } { “vac-1204”: “waiting” } { “vac-1203”: “cleaning” } { “vac-1204”: “waiting” } Alert: Cleaning Alert: Waiting
  15. 15. Three pillars of IoT Things Sense & Act Cloud Storage & Compute Intelligence Insights & Logic → Action AWS Greengrass
  16. 16. Data Lake Architecture
  17. 17. Types of Data • Database records • Search documents • Log files • Messaging events • Devices / sensors / IoT stream Devices Sensors & IoT platforms AWS IoT STREAMS Stream storage IoT COLLECT STORE Mobile apps Web apps Data centers AWS Direct Connect RECORDS Database Applications AWS Import/Export Snowball Logging Amazon CloudWatch AWS CloudTrail DOCUMENTS FILES Search File store LoggingTransport Messaging Message MESSAGES Queue Messaging
  18. 18. Amazon Kinesis Firehose Amazon Kinesis Streams Apache Kafka Amazon DynamoDB Streams Amazon SQS Amazon SQS • Managed message queue service Apache Kafka • High throughput distributed messaging system Amazon Kinesis Streams • Managed stream storage + processing Amazon Kinesis Firehose • Managed data delivery Amazon DynamoDB • Managed NoSQL database • Tables can be stream-enabled Message & Stream Storage Devices Sensors & IoT platforms AWS IoT STREAMS IoT COLLECT STORE Mobile apps Web apps Data centers AWS Direct Connect RECORDS Database Applications AWS Import/Export Snowball Logging Amazon CloudWatch AWS CloudTrail DOCUMENTS FILES Search File store LoggingTransport Messaging Message MESSAGES Messaging Queue Stream
  19. 19. COLLECT STORE Mobile apps Web apps Data centers AWS Direct Connect RECORDS Database AWS Import/Export Snowball Logging Amazon CloudWatch AWS CloudTrail DOCUMENTS FILES Search Messaging Message MESSAGES Devices Sensors & IoT platforms AWS IoT STREAMS Apache Kafka Amazon Kinesis Streams Amazon Kinesis Firehose Amazon DynamoDB Streams Hot Stream Amazon S3 Amazon SQS Message Amazon S3 File LoggingIoTApplicationsTransportMessaging File Storage
  20. 20. Cache, database, search COLLECT STORE Mobile apps Web apps Data centers AWS Direct Connect RECORDS AWS Import/Export Snowball Logging Amazon CloudWatch AWS CloudTrail DOCUMENTS FILES Messaging Message MESSAGES Devices Sensors & IoT platforms AWS IoT STREAMS Apache Kafka Amazon Kinesis Streams Amazon Kinesis Firehose Amazon DynamoDB Streams Hot Stream Amazon SQS Message Amazon Elasticsearch Service Amazon DynamoDB Amazon S3 Amazon ElastiCache Amazon RDS SearchSQLNoSQLCacheFile LoggingIoTApplicationsTransportMessaging
  21. 21. Tools and Frameworks • Machine Learning • Amazon ML, Amazon EMR (Spark ML) • Interactive • Amazon Redshift, Amazon EMR (Presto, Spark) • Batch • Amazon EMR (MapReduce, Hive, Pig, Spark) • Messaging • Amazon SQS application on Amazon EC2 • Streaming • Micro-batch: Spark Streaming, KCL • Real-time: Amazon Kinesis Analytics, Storm, AWS Lambda, KCL Amazon SQS apps Streaming Amazon Kinesis Analytics Amazon KCL apps AWS Lambda Amazon Redshift PROCESS / ANALYZE Amazon Machine Learning Presto Amazon EMR FastSlowFast BatchMessageInteractiveStreamML Amazon EC2 Amazon EC2
  22. 22. STORE CONSUMEPROCESS / ANALYZE Amazon QuickSight Apps & Services Analysis&visualizationNotebooksIDEAPI Applications & API Analysis and visualization Notebooks IDE Business users Data scientist, developers COLLECT ETL
  23. 23. Amazon SQS apps Streaming Amazon Kinesis Analytics Amazon KCL apps AWS Lambda Amazon Redshift COLLECT STORE CONSUMEPROCESS / ANALYZE Amazon Machine Learning Presto Amazon EMR Amazon Elasticsearch Service Apache Kafka Amazon SQS Amazon Kinesis Streams Amazon Kinesis Firehose Amazon DynamoDB Amazon S3 Amazon ElastiCache Amazon RDS Amazon DynamoDB Streams HotHotWarm FastSlowFast BatchMessageInteractiveStreamML SearchSQLNoSQLCacheFileQueueStream Amazon EC2 Amazon EC2 Mobile apps Web apps Devices Messaging Message Sensors & IoT platforms AWS IoT Data centers AWS Direct Connect AWS Import/Export Snowball Logging Amazon CloudWatch AWS CloudTrail RECORDS DOCUMENTS FILES MESSAGES STREAMS Amazon QuickSight Apps & Services Analysis&visualizationNotebooksIDEAPI Reference architecture LoggingIoTApplicationsTransportMessaging ETL
  24. 24. Design Patterns
  25. 25. On-demand Big Data Analytics
  26. 26. Data Warehousing
  27. 27. Smart Applications | Machine Learning
  28. 28. Clickstream Analysis
  29. 29. Three pillars of IoT Things Sense & Act Cloud Storage & Compute Intelligence Insights & Logic → Action AWS Greengrass
  30. 30. 1st Gen: Machine-oriented interactions 2nd Gen: Control-oriented & translated 3rd Gen: Intent-oriented The Advent Of Conversational Interactions
  31. 31. Artificial Intelligence At Amazon (1995)
  32. 32. Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Define New Product Categories Bring Machine Learning To All Artificial Intelligence At Amazon
  33. 33. AI Applications on AWS • Zillow • Zestimate (using Apache Spark) • Howard Hughes Corp • Lead scoring for luxury real estate purchase predictions • FINRA • Anomaly detection, sequence matching, regression analysis, network/tribe analysis • Netflix • Recommendation engine • Pinterest • Image recognition search • Fraud.net • Detect online payment fraud • DataXu • Leverage automated & unattended ML at large scale (Amazon EMR + Spark) • Mapillary • Computer vision for crowd sourced maps • Hudl • Predictive analytics on sports plays • Upserve • Restaurant table mgmt & POS for forecasting customer traffic • TuSimple • Computer Vision for Autonomous Driving • Clarifai • Computer Vision APIs
  34. 34. Algorithms Data Programming Models GPUs & Acceleration The Advent of Deep Learning image understanding natural language processing speech recognition autonomy
  35. 35. One-Click GPU Deep Learning AWS Deep Learning AMI Up to~40k CUDA cores MXNet TensorFlow Theano Caffe Torch Pre-configured CUDA drivers Anaconda, Python3 + CloudFormation template + Container Image
  36. 36. Can We Help Customers Put Intelligence At The Heart Of Every Application & Business?
  37. 37. Amazon AI Intelligent Services Powered By Deep Learning
  38. 38. Origin Destination Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Utterances Flight booking London Heathrow Intent / Slot model London Heathrow
  39. 39. Origin Destination Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Utterances Flight booking London Heathrow Intent / Slot model London Heathrow LocationLocation Seattle
  40. 40. Origin Destination Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Utterances Flight booking London Heathrow Intent / Slot model London Heathrow LocationLocation Seattle Prompt “When would you like to fly?” “When would you like to fly?” Polly
  41. 41. Origin Destination Departure Date Flight Booking London Heathrow Seattle Prompt “When would you like to fly?” “When would you like to fly?” Polly “Next Friday”
  42. 42. Origin Destination Departure Date Flight Booking “Next Friday” Automatic Speech Recognition Next Friday Utterances Natural Language Understanding Flight booking 02 / 24 / 2017 Intent / Slot model London Heathrow Seattle 02/24/2017
  43. 43. Origin Destination Departure Date Flight Booking “Next Friday” Automatic Speech Recognition Next Friday Utterances Natural Language Understanding Flight booking 02 / 24 / 2017 Intent / Slot model London Heathrow Seattle 02/24/2017 Confirmation “Your flight is booked for next Friday” “Your flight is booked for next Friday” Polly
  44. 44. Origin Destination Departure Date Flight Booking “Next Friday” Automatic Speech Recognition Next Friday Utterances Natural Language Understanding Flight booking 02 / 24 / 2017 Intent / Slot model London Heathrow Seattle 02/24/2017 Hotel Booking
  45. 45. Success Stories
  46. 46.  The Philips HealthSuite digital platform analyzes and stores 15 PB of patient data gathered from 390 million imaging studies, medical records, and patient inputs  Running on AWS provides the reliability, performance and scalability that Philips needs to help protect patient data as its global digital platform grows at the rate of one petabyte per month. AWS Customers making an impact with IoT Video Testimonial
  47. 47. The BMW Group  Connected-car application collects sensor data from BMW 7 Series cars to give drivers dynamically updated map information.  Built its new car-as-a-sensor (CARASSO) service in only six months  CARASSO can adapt to rapidly changing load requirements; can scale up and down by two orders of magnitude within 24 hours.  By 2018 CARASSO is expected to process data collected by a fleet of 100,000 vehicles traveling more than eight billion kilometers. AWS Customers making an impact with IoT Video Testimonial
  48. 48. “For our market surveillance systems, we are looking at about 40% [savings with AWS], but the real benefits are the business benefits: We can do things that we physically weren’t able to do before, and that is priceless.” - Steve Randich, CIO Analytics Case Study: Re-architecting Compliance What FINRA needed • Infrastructure for its market surveillance platform • Support of analysis and storage of approximately 75 billion market events every day Why they chose AWS • Fulfillment of FINRA’s security requirements • Ability to create a flexible platform using dynamic clusters (Hadoop, Hive, and HBase), Amazon EMR, and Amazon S3 Benefits realized • Increased agility, speed, and cost savings • Estimated savings of $10-20m annually by using AWS
  49. 49. • Began implementing an S3 data lake on AWS in 2014 • Has been running in production since early 2015 • Now able to integrate all data sets together in one analytics platform, i.e. sales data, marketing data, manufacturing line data, patient population data, FDA public datasets, etc. • Can easily share this aggregated data across all business units • Rapid data experimentation • Enables new use cases & data innovations not previously possible • Leverages Amazon EMR and Amazon Redshift for their analytics layer around the lake • Leverages R-Studio and SAS for data science layer on top of EMR and Redshift • They use EMR for their ETL layer • EMR is 50% faster & 30% cheaper than their legacy ETL solution • Amgen’s AWS S3 data lake won Best Practice Award at Bio-IT World 2016 for ‘Real World Data Platform & Analytics’ Best Practices Awards Bio IT World 2016 Winner
  50. 50. Thank you!

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