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Opening Keynote

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Speaker: Dr. Werner Vogels, Chief Technology Officer, Amazon.com

AWS Customer Speakers:
- Jonathan Sudharta, Chief Executive Officer, HaloDoc
- Sergei Shvetsov, Head of Engineering, Cloud Infrastructure, Traveloka
- Denni Gautama, VP of Engineering, Travel Products, Traveloka

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Opening Keynote

  1. 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Keynote Address Dr. Werner Vogels Vice President & CTO, Amazon.com
  2. 2. Image by Guido van Nispen in the Dutch Digital Pioneer collection Dr. Werner Vogels CTO Amazon.com @werner
  3. 3. “The reality, of course, today is that if you come up with a great idea you don't get to go quickly to a successful product. There's a lot of undifferentiated heavy lifting that stands between your idea and that success." -Jeff Bezos
  4. 4. Largest Number of Startups
  5. 5. AWS has changed the cost of starting a business radically
  6. 6. Investments by VCs have doubled since the inception of AWS
  7. 7. More bets with significantly higher payoffs
  8. 8. Capital shifts to later stages with less risk.
  9. 9. Denni Gautama VP of Engineering Sergei Shvetsov Head of Engineering, Cloud Infrastructure
  10. 10. Keynote: AWS Startup Day Jakarta 2018 Denni Gautama - VP of Engineering, Travel Products Sergei Shvetsov - Head of Engineering, Cloud Infrastructure
  11. 11. Raising a SEA Tech Unicorn, a Traveloka Story Denni Gautama VP of Engineering, Travel Products
  12. 12. To enrich people’s lives by enabling them to do more and go beyond traveling. #EnablingMobility #CreateMoments Our mission
  13. 13. Traveloka goes regional ~ 600 people ~ 100 engineers Founded as a flight meta- search Eight (8) people 2012 2013 2014 2015 Pivot to OTA (Flight) - Hotel - Mobile App ~ 200 people 2017 - Train, Attraction & Activities, Prepaid top up & data package, Movies - SG & IN Dev Centers ~1,500 people ~300 engineers 2018 and beyond - Eats, Car Rental - 40+ million downloads 2,000+ people ~500+ engineers Our Brief History 2016
  14. 14. Traveloka’s products and services 100+Airline partners (FSC & LCC) 450.000+ hotels in 100 countries
  15. 15. Riding on AWS Platform Sergei Shvetsov Head of Engineering, Cloud Infrastructure
  16. 16. Benefits of AWS • Bring new products to market faster than ever • Do so much more, with a lot less effort • Elasticity, scalability, reliability, security
  17. 17. September 2015 September 2018 AWS Services 9 37 EC2 390 1938 ELB 1 805 RDS 4 313 ElastiCache 1 283 Elasticsearch 0 51 How our Infra has grown
  18. 18. Thank you for your time
  19. 19. $5,000,000 Team of Engineers Months of development $5,000 An Engineer Weeks of development Launching an Idea 2005 2018
  20. 20. Cloudfront Lambda ECS Poly, Rekognition, Lex RDS Route53 Elastic Beanstalk AWS Data Pipeline Kinesis 516 24 48 61 82 159 280 722 1,017 LAUNCHES 20 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 1,400+ 2 0 1 7 P A C E O F I N N O V A T I O N NEW CAPABILITIES DAILY SageMaker, Comprehend, Fargate Startups Have Evolved
  21. 21. Startup A 2010 AWS Startup B 2018 AWS A Tale Of Two Startups
  22. 22. 1. Focused on releasing a MVP 2. Manual provisioning of servers 3. Manual deploy process 4. Limited monitoring 5. Averaging a deploy 1-2x a week 6. Dev on-boarding: 10-14 days Startup A 2010 AWS Idea / Early Stage 2010 Startup: Day One
  23. 23. 1. Focused on productivity 2. IaC, Automation, Scalability 3. Deployment Pipelines 4. Improved Monitoring 5. Averaging a deploy: 2-3x a day 6. Engineering onboarding: 7 days Stabilization Stage 2010 Startup: Year One Startup A 2010 AWS
  24. 24. 1. Focused on growth 2. Systems automated, auto scaling 3. Automated deploy processes 4. In-depth monitoring and alerting 5. Averaging a deploy: 15+ a day 6. Engineering onboarding: 1-3 Days Growth 2010 Startup: Year Two Startup A 2010 AWS
  25. 25. Engineers Deploys a Day Onboarding Time Goal of Engineering Productivity
  26. 26. We’ve been working closely with startups and enterprises for 11 years.
  27. 27. Cloudfront Lambda ECS Poly, Rekognition, Lex RDS Route53 Elastic Beanstalk AWS Data Pipeline Kinesis 516 24 48 61 82 159 280 722 1,017 LAUNCHES 20 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 1,400+ 2 0 1 7 P A C E O F I N N O V A T I O N NEW CAPABILITIES DAILY SageMaker, Comprehend, Fargate Startups Have Evolved
  28. 28. Startup B 2018 AWS
  29. 29. 1. Focused on product and growth 2. Systems automated, auto scaling 3. Automated deploy processes 4. In-depth monitoring and alerting 5. Engineering onboarding: 1-3 Day Startup B 2018 AWS 2018 Startup: Day One
  30. 30. Real-time delivery of in-game data, handling 45 billion events per day. Kinesis
  31. 31. P U S H I N G T H E B O U N D A R I E S O F S C A L E
  32. 32. V P C GAME Amazon ECS Amazon Athena Amazon ELB Amazon EMR Amazon GuardDuty Amazon S3 Amazon SNS Amazon SQS AWS Device Farm AWS KMS Amazon Kinesis PLATFORM ANALYTICS Client Servers Games services • Sta ts • Profi l e • C l ou d stora ge • Ma tch making Identity eCommerce • Accou n t • Pa rti es • C h a t • Pa ymen ts • C a ta l og • En ti tl emen ts Amazon Aurora Epic’s architecture at a glance
  33. 33. R E A L - T I M E I N G E S T I O N D A T A W A R E H O U S E M A N A G E D C L U S T E R S 92M e ve n ts/ m in ute ~54B e ve n ts/ da y 40GB / m in ute o f da ta a t p e a k 5,000 Kin e sis sh a rds 14 p e ta b yte s ~2PB m o n th ly gro w th ra te ~8.3K b a tch ETL jo b s/ da y 22 EMR cluste rs in p ro d +4K EC2 a n a lytics in sta n ce s Analytics pipeline stats
  34. 34. Other sources APIs Databases N E A R R E A L T I M E P I P E L I N E B A T C H P I P E L I N E Hive Game clients Game servers Launcher Game services A M A Z O N K I N E S I S Grafana Scoreboards API Limited Raw Data (real time ad-hoc SQL) Spark streaming on EMR Tableau/BI Ad-hoc SQL select * from fortnite_base where ... A M A Z O N D Y N A M O D B ( M e t r i c s t o r a g e ) User ETL (Metri c defi n i ti on ) S 3 Batch ETL on EMR S 3 Analytics pipeline architecture
  35. 35. SE RVICE HE A LTH TO URNAME NTS BASIC K PIs GAME A NA LYSIS Using analytics to improve business outcomes
  36. 36. Object-level controlsUnmatched durability availability, and scalability Best security, compliance, and audit capabilities Business insights into your data Most ways to bring data in Twice as many partner integrations Amazon S3 Is The Most Popular Choice For Data Lakes
  37. 37. COMPUTE WRITE TIME READ TIME 41m 40s 13m 52s S3 5 T B o f 2 M B o b j e c t s w i t h 1 p r e f i x S3 For Data Analytics BEF O RE:
  38. 38. COMPUTE WRITE TIME READ TIME 12m 00s 7m 00s 5 T B o f 2 M B o b j e c t s w i t h 1 p r e f i x S3 S3 Request Performance Increase NO W :
  39. 39. COMPUTE 0h 1m 12s 0h 0m 42s x10S3 P A RALLEL P RO C ESS ING: 5 T B o f 2 M B o b j e c t s a c r o s s 1 0 p r e f i x e s WRITE TIME READ TIME S3 Request Performance Increase
  40. 40. AWS GLUE S t o r a g e N o n - r e l a t i o n a l D a t a b a s e s A U R O R A C O M M E R C I A L C O M M U N I T Y R e l a t i o n a l D a t a b a s e s Amazon DynamoDB Amazon ElastiCache Amazon Athena Amazon EMR Hadoop, Spark, Presto, Pig, Hive…19 total Amazon Redshift + Redshift Spectrum Amazon Elasticsearch Service Amazon Kinesis Amazon QuickSight A n a l y t i c s Amazon CloudSearch AWS Data Pipeline M a c h i n e L e a r n i n g Amazon S3 Amazon EFS Amazon EBS Amazon Glacier O n - P r e m i s e s D a t a b a s e s AWS Glue Helps You Get The Most Out Of Your Data
  41. 41. Put Machine Learning in the hands of every developer
  42. 42. Personalized recommendations Inventing entirely new customer experiences Fulfillment automation / inventory management Drones Voice driven interactions Machine Learning at Amazon: A long heritage
  43. 43. RETAIL Demand Forecasting Vendor Lead Time Prediction Pricing Packaging Substitute Prediction CUSTOMERS Recommendation Product Search Product Ads Shopping Advice Customer Problem Detection SELLER Fraud Detection Predictive Help Seller Search & Crawling CATALOGUE Browse-Node Classification Meta-data Validation Review Analysis Product Matching TEXT In-Book Search Named-entity Extraction Summarization/X-ray Plagiarism Detection IMAGES Visual Search Product Image Enhancement Brand Tracking THOUSANDS OF EMPLOYEES ACROSS THE COMPANY FOCUSED ON AI Machine Learning at Amazon.com
  44. 44. COUNTERFEIT GOODS DETECTION CUSTOMER ADOPTION MODELS ITEM CLASSIFICATION SALES LEAD RANKING SEARCH INTENT DEMAND ESTIMATION CUSTOMER SUPPORT DISPLAY ADS The spark for hundreds of new smart applications within Amazon.com
  45. 45. 8 out of 10 of all ML workloads run on AWS 250% growth YoY 10s of thousands of active developers running ML on AWS TENSORFLOW ON AWS Nucleus research, December 2017 - Report R206
  46. 46. Tens Of Thousands Of Customers Running Machine Learning On AWS
  47. 47. “BMS understands the importance of technologies like machine learning, which can further enable efforts to discover, develop and deliver innovative medicines that help patients prevail over serious diseases. For example, our data scientists use AWS tools like SageMaker to accelerate their modeling work.” – Paul von Autenried, CIO
  48. 48. Jonathan Sudharta CEO HaloDoc
  49. 49. Elevate the healthcare experience. purpose Simplifying access to healthcare. mission
  50. 50. 260 mio populations The 4th most populous country 17,508 islands The largest archipelago in the world INDONESIA
  51. 51. 74,093 villages Large numbers of villages with poor access to healthcare Castrol Index #1 Jakarta Urban cities access to healthcare impacted by traffic issues INDONESIA
  52. 52. Per 10,000 population Physicians World 13.9 / 10,000 UK 28.1 / 10,000 USA 24.5 / 10,000 Singapore 19.5 / 10,000 Malaysia 12.0 / 10,000 Thailand 3.9 / 10,000 Indonesia 3.0 / 10,000 Challenges that look particularly acute when assessing healthcare. INDONESIA
  53. 53. Cardiologist 1:260,000 1,000 Doctors 260 Million Population INDONESIA
  54. 54. Commute to Hospital Consultation Payments Administration Waiting Room Commute to Pharmacy Receive Medicine Commute to home INDONESIA Total time for traditional patient journey 4 HOURS
  55. 55. Launched on April 2016
  56. 56. Key Stakeholder Support
  57. 57. Traditional patient journey 4 HOURS Patient at Home Find Doctor Consultation E-Prescription Payments Medicine Delivery Halodoc patient journey 35 MINS Halodoc Solving Pain
  58. 58. Apotik Doctors 3.e-prescribe 3. e-prescribe 1. consultation and telemedicine 2. scheduling 3. e-prescribe 4. e-commerce on shared economy 3.e-prescribe Lab 1. consultation and telemedicine 2. scheduling The Ecosystem
  59. 59. One Platform – All your healthcare needs Contact Doctor Pharmacy Delivery Lab Service Adherence Articles Cashless eRx Patient Medical History Patient Medical History Product Journey
  60. 60. Pharmacy Delivery Lab Service Adherence Articles Cashless eRx Patient Medical History Contact Doctor Select your doctor & get into a consultation with the click of a button. Chat, Call, Video - for you to directly consult with the doctor. Get recommendation, prescription or referral as per your need. Product Journey
  61. 61. Use the prescription coming from the online consultation or simply upload a photo of the one you got from your doctor outside - to get medicines delivered. Pharmacy Delivery Lab Service Adherence Articles Cashless Patient Medical History Contact Doctor eRx Product Journey
  62. 62. Lab Service Adherence Articles Cashless Patient Medical History Contact Doctor eRx Pharmacy Delivery Search for medicines you need, upload a prescription or browse for any health-care products. Find exclusive deals. Pay by CoD or with the money in your HD Wallet. Product Journey
  63. 63. Lab Service Articles Cashless Patient Medical History Contact Doctor eRx Pharmacy Delivery Got medicines to take or a prescription to adhere to - with the click of a button, add reminders on Halodoc, to ensure you never miss a dose. You can also view your adherence report, to track the progress & manage your condition better. Adherence Product Journey
  64. 64. Lab Service Cashless Patient Medical History Contact Doctor eRx Pharmacy Delivery Adherence Articles Curious about a medical condition or just want to live a healthier life – Articles on Halodoc has it all covered. Get customized notification with content specifically designed for you & your enlightenment. Product Journey
  65. 65. Cashless Patient Medical History Contact Doctor eRx Pharmacy Delivery Adherence Articles Lab Service Specially curated test packages - all with the convenience of time & location, powered by Prodia. Get your test results when they’re ready & view them on Halodoc. Product Journey
  66. 66. Patient Medical History Contact Doctor eRx Pharmacy Delivery Adherence Articles Lab Service Link your insurance, & get direct benefits of your policy coverage on Halodoc. All without the hassle of any wait or claim-filing. Cashless Product Journey
  67. 67. Contact Doctor eRx Pharmacy Delivery Adherence Articles Lab Service Cashless Patient Medical History One Platform - All your healthcare needs. All records in one place. For you & your family. Your digital healthcare passport. Product Journey
  68. 68. 2Q2017 3Q2017 4Q2017 1Q2018 2Q2018 3Q2018E2Q2017 3Q2017 4Q2017 1Q2018 2Q2018 3Q2018E Platform set for strong growth given rapid uptake in users completing consultations and returning Monthly Consultations Completed (000) Average Daily Transactions 12,000+ GPs 8,000+ Specialists 50 In-House Doctors for Continuous Availability 7.6x 7.5x Teleconsultation
  69. 69. Strong traction in pharmacy delivery service given back-end integration with Go-Jek and Blibli 14,000+ SKUs 10,000+ Pharmacies in Delivery Network 30+ Cities and Municipalities served Pharmacy Delivery Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18 Aug-18 6.2x 9x 2Q2017 3Q2017 4Q2017 1Q2018 2Q2018 3Q2018E Transaction Volume (000) B2C GMV
  70. 70. AWS forms the plumbing of “health care data network” including 1000+ pharmacies, hundreds of hospitals and thousands of Doctor partners. Reliable Partner • Faster time to market, critical to us as a start up • Reduced time maintaining Infra, allowing us to focus on features user love. • Scalability made easy, allowing for the strong month Halodoc is “all-in” AWS on cloud.
  71. 71. Overall Architecture Components 1. ALBs for Load Distribution 2. EC2 instances hosting the application 3. RDS MySQL instances for relational database storage. 4. DynamoDB for key-value data storage a. Server event pipeline (detailed later) for async backup of key-value data to relational store 5. S3 for long term data archival and storage. 6. Athena/Redshift for data query and analysis
  72. 72. Sample - Server Events System SNS based pipeline DynamoDB to store event configurations. 1 SNS topic per event type generated. Multiple consumers for each SNS topic. Kinesis Firehose delivery Streams for S3 delivery. Kinesis based pipeline DynamoDB to store event configurations. Multiple Kinesis streams per event type generated. Lambda and Firehose streams as consumers for streams. Lambda for record transformation and S3 delivery.
  73. 73. Mensa 2 Building, HR Rasuna Said B34, Jakarta 12940 – Indonesia www.halodoc.com
  74. 74. Put Machine Learning in the hands of every developer
  75. 75. ML FRAMEWORKS
  76. 76. ML PLATFORMS A M A Z O N S A G E M A K E R A W S D E E P L E N S ML FRAMEWORKS
  77. 77. Train with SageMaker algorithms Train with your own algorithms Train with TensorFlow and MXNet A/B testOptimize your models Host models Amazon SageMaker: Custom ML Models Using Your Own Data
  78. 78. Amazon SageMaker: Built-In Algorithms Designed For “Infinite Scale” Object detection Multi-class classifiers Deep forecasting Text classification Anomaly detection PCA, kNN, regression, boosted trees…
  79. 79. Object detection Multi-class classifiers Deep forecasting Text classification Anomaly detection PCA, kNN, regression, boosted trees… Designed to be 10X better Streaming data Single pass training Checkpointing Available as an API 10x API Amazon SageMaker: Built-In Algorithms Designed For “Infinite Scale”
  80. 80. “How can we make our algorithms as fast as those in SageMaker?”
  81. 81. ACCELERATE YOUR OWN ALGORITHMS BY STREAMING LARGE VOLUMES OF TRAINING DATA FROM AMAZON S3 A m a z o n S a g e M a k e r S t r e a m i n g A l g o r i t h m s
  82. 82. SageMaker Streaming For Custom Algorithms Faster training Stream data to your own algorithm Quicker time to start training Lower cost training
  83. 83. Faster training Stream data to your own algorithm Quicker time to start training Additional frameworks coming soon Lower cost training TensorFlow SageMaker Streaming For Custom Algorithms
  84. 84. A/B testOptimize your models Host modelsTrain with SageMaker algorithms Train with your own algorithms Train with TensorFlow and MXNet Amazon SageMaker: Custom ML Models Using Your Own Data
  85. 85. A/B testOptimize your models Host modelsTrain with SageMaker algorithms Train with your own algorithms Train with TensorFlow and MXNet Amazon SageMaker: Custom ML Models Using Your Own Data
  86. 86. Low latency Easy to integrate API endpoint Auto-scaling Fault tolerant, multi-AZ Amazon SageMaker: Elastic Hosting For Custom Models
  87. 87. Low latency Easy to integrate API endpoint Auto-scaling Fault tolerant, multi-AZ What if your files are big? What if you need to batch process? But… Amazon SageMaker: Elastic Hosting For Custom Models
  88. 88. RUN FULLY MANAGED, HIGH -THROUGHPUT BATCH TRANSFORM JOBS WITH A SIMPLE API CALL A m a z o n S a g e M a k e r B a t c h T r a n s f o r m
  89. 89. Batch test models before hosting Process data dumps in a batch Process large files more easily Test models before deployment at the edge Batch Processing For Amazon SageMaker
  90. 90. Batch test models before hosting Process data dumps in a batch Process large files more easily Test models before deployment at the edge Reuse pre-processing pipelines between training and prediction Use same model for batch and real-time Fully managed batch transforms Batch Processing For Amazon SageMaker
  91. 91. AI SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X Vision Speech Language Chatbots & Contact Centers ML PLATFORMS A M A Z O N S A G E M A K E R A W S D E E P L E N S ML FRAMEWORKS
  92. 92. A m a z o n L e x A m a z o n T r a n s c r i b e A m a z o n C o m p r e h e n d TR A N S CR I P T A m a z o n C o n n e c t Analytics Improving Contact Centers With Artificial Intelligence
  93. 93. Improving Contact Centers With Artificial Intelligence A m a z o n L e x A m a z o n T r a n s c r i b e A m a z o n C o m p r e h e n d TR A N S CR I P T A m a z o n C o n n e c t Analytics
  94. 94. FULLY AUTOMATED MULTI-CHANNEL AUDIO TRANSCRIPTION C h a n n e l S y n t h e s i s F o r A m a z o n T r a n s c r i b e
  95. 95. A M A Z O N T R A N S C R I B E C u s t o m e r A g e n t T R A N S C R I B E T R A N S C R I B E S y n c h r o n i z e T r a n s c r i p t s Si ngl e Transc ri p t Channel Synthesis For Amazon Transcribe
  96. 96. Improving Voice Of Customer Analytics With Artificial Intelligence A M A Z O N C O M P R E H E N D A M A Z O N T R A N S L A T E A M A Z O N S 3 A M A Z O N Q U I C K S I G H T A M A Z O N A T H E N A S e n t i m e n t , e n t i t i e s , k e y p h r a s e s S O C I A L M E D I A a n d S U P P O R T C H A N N E L S
  97. 97. JAPANESE, RUSSIAN, ITALIAN, CHINESE (TRADITIONAL), TURKISH, AND CZECH Amazon Translate: 12 New Language Pairs C O M I N G S O O N DUTCH, SWEDISH, POLISH, DANISH, HEBREW, AND FINNISH 12 more!
  98. 98. Improving Voice Of Customer Analytics With Artificial Intelligence A M A Z O N C O M P R E H E N D A M A Z O N T R A N S L A T E A M A Z O N S 3 A M A Z O N Q U I C K S I G H T A M A Z O N A T H E N A S e n t i m e n t , e n t i t i e s , k e y p h r a s e s S O C I A L M E D I A a n d S U P P O R T C H A N N E L S 24 language pairs
  99. 99. FINE-GRAINED TEXT ANALYSIS Amazon Comprehend Syntax Identification
  100. 100. "I love my yellow Fire Tablet” P h i l ’ s R e v i e w s Amazon Comprehend Syntax Identification
  101. 101. "I love my yellow Fire Tablet” P h i l ’ s R e v i e w s Amazon Comprehend Syntax Identification Sentiment: positive Entity: Fire Tablet
  102. 102. Kindle Fire POSITIVE NEGATIVE "I love my yellow Fire Tablet” P h i l ’ s R e v i e w s Amazon Comprehend Syntax Identification Sentiment: positive Entity: Fire Tablet
  103. 103. P h i l ’ s R e v i e w s Amazon Comprehend Syntax Identification Sentiment: positive Entity: Fire Tablet Adjective: Yellow "I love my yellow Fire Tablet”
  104. 104. Kindle Fire: Positive Sentiment: positive Entity: Fire Tablet Adjective: Yellow Canary Yellow Black Punch Red Marine Blue "I love my yellow Fire Tablet” P h i l ’ s R e v i e w s Amazon Comprehend Syntax Identification
  105. 105. AI SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X Vision Speech Language Chatbots & Contact Centers ML PLATFORMS A M A Z O N S A G E M A K E R A W S D E E P L E N S ML FRAMEWORKS
  106. 106. AWS has shipped over 100 new services and major new features for machine learning since re:Invent 2017 A m a z o n P o l l y W o r d P r e s s p l u g i n i n t e g r a t e s T r a n s l a t e A m a z o n R e k o g n i t i o n I m a g e a n d V i d e o l a u n c h i n T o k y o a n d S y d n e y A m a z o n T r a n s c r i b e - G A A m a z o n T r a n s l a t e - G A A n o m a l y D e t e c t i o n ( R a n d o m C u t F o r e s t ) A l g o r i t h m A u t o m a t e r o l e c r e a t i o n d u r i n g s e t u p A u t o m a t i c M o d e l T u n i n g A u t o s c a l i n g c o n s o l e A W S D e e p L e n s d e v i c e s a v a i l a b l e f o r s a l e B l a z i n g T e x t A l g o r i t h m B u i l t - i n A l g o r i t h m s P i p e M o d e S u p p o r t C a f f e t o M X N e t c o d e t r a n s l a t o r C h a i n e r p r e - b u i l t c o n t a i n e r C l o u d F o r m a t i o n s u p p o r t C l o u d T r a i l i n t e g r a t i o n f o r a u d i t l o g s C U D A 9 . 0 a n d 9 . 1 + c u D N N 7 . 1 . 4 + G P U D r i v e r 3 9 0 . 4 6 + N C C L 2 . 2 . 1 3 u p g r a d e C u s t o m e r V P C s u p p o r t f o r t r a i n i n g a n d h o s t i n g D C A : A W S D e e p L e a r n i n g A M I i n D C A D e e p A R a l g o r i t h m D e e p L e a r n i n g A M I i n G o v C l o u d D e e p L e a r n i n g A M I : A d d C h a i n e r , M X N e t 1 . 1 s u p p o r t ( i n c l . B J S ) D e e p L e a r n i n g A M I : E C 2 O p t i m i z e d B i n a r i e s f o r T e n s o r F l o w D e e p L e a r n i n g A M I : O p t i m i z e d C h a i n e r 4 , C N T K 2 . 5 D e e p L e a r n i n g A M I : R e f r e s h f o r E C 2 o p t i m i z e d T e n s o r F l o w 1 . 8 D e e p L e a r n i n g A M I : R e f r e s h w i t h T F 1 . 5 a n d M X N e t 1 . 1 D e e p L e a r n i n g A M I : R e g i o n a l E x p a n s i o n i n 5 p u b l i c r e g i o n s : U S W e s t ( C a l i f o r n i a ) , C a n a d a , S a o P a u l o , L o n d o n , a n d P a r i s D e e p L e a r n i n g A M I : T e n s o r F l o w m u l t i - g p u o p t i m i z a t i o n s u s i n g H o r o v o d , N C C L a n d O p e n M P ID e e p L e a r n i n g A M I : T F 1 . 5 R C 0 , M M S , T F S e r v i n g , T e n s o r B o a r d ( i n c l u d i n g B J S )F P 1 6 s u p p o r t D e e p L e n s i n t e g r a t i o n w i t h A m a z o n K i n e s i s v i d e o s t r e a m E x p o r t / I m p o r t A m a z o n L e x b o t s c h e m a F a c e R e c o g n i t i o n v 3 l a u n c h G D P R C o m p l i a n c e G r a d i e n t C o m p r e s s i o n I m p o r t f r o m S a g e M a k e r t o D e e p L e n s I n c r e a s e C h a r a c t e r L i m i t I n t e r n e t - f r e e n o t e b o o k i n s t a n c e s K M S s u p p o r t f o r t r a i n i n g a n d h o s t i n g L i n e a r L e a r n e r I m p r o v e m e n t s - E a r l y S t o p p i n g , N e w L o s s F u n c t i o n s , a n d C l a s s W e i g h t s m l . p 3 . 2 x l a r g e n o t e b o o k i n s t a n c e s M o d e l O p t i m i z e r M o r e i n s t a n c e t y p e s s u p p o r t M X N e t M o d e l S e r v e r M X N e t M o d e l S e r v e r M X N e t M o d e l S e r v e r v 0 . 2 M X N e t M o d e l S e r v e r v 0 . 3 M X N e t v 1 . 0 r e l e a s e M X N e t v 2 . 0 r e l e a s e n b e x a m p l e s u p p o r t i n S a g e M a k e r n o t e b o o k i n s t a n c e s A m a z o n L e x : D T M F S u p p o r t N E R , K e y p h r a s e , S e n t i m e n t B a t c h N e w B r e a t h S S M L t a g N e w F r e n c h V o i c e ( f e m a l e ) N e w P h o n a t i o n S S M L t a g N o t e b o o k b o o t s t r a p s c r i p t O N N X v 1 . 0 a n n o u n c e m e n t P C I D S S C o m p l i a n c e P o l l y i n G o v C l o u d P o l l y v o i c e s i n A l e x a S k i l l s P r i v a t e L i n k s u p p o r t f o r S a g e M a k e r i n f e r e n c i n g A P I s P y T o r c h p r e - b u i l t c o n t a i n e r R e f r e s h D L A M I i n D C A R e g i o n e x p a n s i o n t o F R A R e g i o n e x p a n s i o n t o N R T R e g i o n e x p a n s i o n t o S y d n e y R e k o g n i t i o n H I P A A s u p p o r t R e k o g n i t i o n V i d e o i n A W S G o v C l o u d S a g e M a k e r r e g i o n e x p a n s i o n t o I C N S t o r e t r a n s c r i p t i o n o u t p u t i n c u s t o m e r S 3 b u c k e t s S u p p o r t f o r K e r a s 2 . 0 S u p p o r t G l u o n m o d e l s T a g - b a s e d a c c e s s c o n t r o l T e n s o r F l o w 1 . 5 , M X N e t 1 . 0 , a n d C U D A 9 S u p p o r t T e n s o r F l o w 1 . 6 a n d M X N e t 1 . 1 C o n t a i n e r s T e n s o r F l o w 1 . 7 C o n t a i n e r s T e n s o r F l o w 1 . 8 C o n t a i n e r T e n s o r f l o w a n d C a f f e s u p p o r t T e n s o r F l o w a n d M X N e t C o n t a i n e r s - O p e n S o u r c i n g a n d L o c a l M o d e T r a i n i n g J o b c l o n i n g i n c o n s o l e T r a n s c r i b e i n t e g r a t i o n w i t h C l o u d T r a i l T r a n s c r i b e i n t e g r a t i o n w i t h C l o u d W a t c h D L A M I r e f r e s h w i t h M X N e t 1 . 0 , T F f o r C U D A 9 a n d P y T o r c h f o r C U D A 9 H I P A A c o m p l i a n c eT r a n s l a t e - - a d d t o P o l l y W o r d P r e s s p l u g i n T r a n s l a t e - - G D P R c o m p l i a n c e D e e p L e a r n i n g A M I : R e l e a s e l a t e s t D L A M I v e r s i o n i n B J S D L A M I l a u n c h i n B J S a n d F R A , B O M , S I N N C C L s u p p o r tT r a n s l a t e - - G o v C l o u d T r a n s l a t e - - M o b i l e S D K A m a z o n R e k o g n i t i o n I m a g e a n d V i d e o l a u n c h i n U S E a s t ( O h i o ) A d v a n c e d I n d e x i n g S u p p o r t A m a z o n L e x r e g i o n s u p p o r t A m a z o n L e x : D e f a u l t R e s p o n s e s A m a z o n P o l l y W o r d P r e s s P l u g i nX G B o o s t I n s t a n c e W e i g h t s
  107. 107. How do you get started?
  108. 108. Go to ml.aws
  109. 109. Join AWS User Group https://www.facebook.com/groups/awsindonesia/
  110. 110. GO BUILD!

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