Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

AWS Re:Invent 2019 Re:Cap

2.871 visualizaciones

Publicado el

AWS Re:Invent 2019 Re:Cap

Publicado en: Software
  • Inicia sesión para ver los comentarios

AWS Re:Invent 2019 Re:Cap

  1. 1. Chris Fregly Developer Advocate, AI & Machine Learning Amazon Web Services @ San Francisco @cfregly Re:Invent December 2019 65,000 Attendees 3,000 Sessions https://aws.amazon.com/new/reinvent/
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. https://aws.amazon.com/new/reinvent/
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda • Improving the Developer Experience • Compute • Storage • AI/ML • Database & Analytics • Networking • Security • Extending AWS beyond the Region
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  5. 5. Improving the Developer Experience
  6. 6. L E A R N M O R E SVS401 - Optimizing your serverless applications Provisioned Concurrency on AWS Lambda New Feature • Keeps functions initialized and hyper-ready, ensuring start times stay in the milliseconds • Builders have full control over when provisioned concurrency is set • No code changes are required to provision concurrency on functions in production • Can be combined with AWS Auto Scaling at launch DRAFTServerless General Availability – December 3
  7. 7. Achieve up to 67% cost reduction and 50% latency reduction compared to REST APIs. HTTP APIs are also easier to configure than REST APIs, allowing customers to focus more time on building applications. Reduce application costs by up to 67% Reduce application latency by up to 50% Configure HTTP APIs easier and faster than before HTTP APIs for Amazon API Gateway Introducing DRAFTMobile Services Preview – December 4 L E A R N M O R E CON213-L - Leadership session: Using containers and serverless to accelerate modern application development (incl schema registry demo)
  8. 8. AWS Step Functions Express Workflows Introducing Orchestrate AWS compute, database, and messaging services at rates greater than 100,000 events/second, suitable for high-volume event processing workloads such as IoT data ingestion, streaming data processing and transformation. DRAFTApp Integration General Availability – December 3 L E A R N M O R E API321: Event-Processing Workflows at Scale with AWS Step Functions
  9. 9. Amazon EventBridge Schema Registry Introducing Store event structure - or schema - in a shared central location, so it’s faster and easier to find the events you need. Generate code bindings right in your IDE to represent an event as an object in code. DRAFTApp Integration Preview – December 3 LEARN MORE CON213-L - Leadership session: Using containers and serverless to accelerate modern application development (incl schema registry demo)
  10. 10. Amplify for iOS & Android Introducing DRAFTMobile Services General Availability – December 3 Open source libraries and toolchain that enable mobile developers to build scalable and secure cloud powered serverless applications. L E A R N M O R E MOB317 - Speed up native mobile development with AWS Amplify
  11. 11. Amplify DataStore New Feature DRAFTMobile Services General Availability – December 3 Multi-platform (iOS/Android/React Native/Web) on-device persistent storage engine that automatically synchronizes data between mobile/web apps and the cloud using GraphQL. L E A R N M O R E MOB402: Build data-driven mobile and web apps with AWS AppSync
  12. 12. Compute
  13. 13. Amazon EC2 Inf1 Instances Introducing The fastest and lowest cost machine learning inference in the cloud Featuring AWS Inferentia, the first custom ML chip designed by AWS Inf1 delivers up to 3X higher throughput and up to 40% lower cost per inference compared to GPU powered G4 instances Compute General Availability – December 3 L E A R N M O R E CMP324-R: Deliver high performance ML inference with AWS Inferentia Natural language processing PersonalizationObject detection Speech recognition Image processing Fraud detection
  14. 14. AWS Graviton2 Processor Introducing Enabling the best price/performance for your cloud workloads Graviton1 Processor Graviton2 Processor DRAFTCompute Preview – December 3 L E A R N M O R E CMP322-R: Deep dive on EC2 instances powered by AWS Graviton
  15. 15. AWS Graviton2 Based Instances Introducing Up to 40% better price-performance for general purpose, compute intensive, and memory intensive workloads. l M6g C6g R6g DRAFT Built for: General-purpose workloads such as application servers, mid-size data stores, and microservices Instance storage option: M6gd Built for: Compute intensive applications such as HPC, video encoding, gaming, and simulation workloads Instance storage option: C6gd Built for: Memory intensive workloads such as open-source databases, or in-memory caches Instance storage option: R6gd Compute Preview – December 3 L E A R N M O R E CMP322-R: Deep dive on EC2 instances powered by AWS Graviton
  16. 16. Amazon Braket Introducing Fully managed service that makes it easy for scientists and developers to explore and experiment with quantum computing. DRAFTQuantum Technology Preview – December 2 LEARN MORE CMP213: Introducing Quantum Computing with AWS
  17. 17. AWS Nitro Enclaves Introducing Create additional isolation to further protect highly sensitive data within EC2 instances Nitro Hypervisor Instance A Enclave A Instance B EC2 Host Additional isolation within an EC2 instance Isolation between EC2 instances in the same host Local socket connection DRAFTCompute Preview – December 3
  18. 18. AWS Compute Optimizer Introducing Identify optimal EC2 instances and Auto Scaling group with a ML- powered recommendation engine. Integrated with AWS Organizations. DRAFTManagement Tools General Availability – December 3 LEARN MORE CMP323-R: Optimize performance and cost for your AWS compute
  19. 19. AWS Compute Optimizer
  20. 20. Receive lower rates automatically. Easy to use with recommendations in AWS Cost Explorer Significant savings of up to 72% Flexible across instance family, size, OS, tenancy or AWS Region; also applies to AWS Fargate & soon to AWS Lambda usage Compute/Cost Management LEARN MORE CMP210: Dive deep on Savings Plans Announced – November 6 Simplify purchasing with a flexible pricing model that offers savings of up to 72% on Amazon ECS, AWS Fargate & AWS Lambda usage Savings Plans
  21. 21. DRAFTContainers General Availability – December 3 LEARN MORE CON-326R - Running Kubernetes Applications on AWS Fargate Introducing The only way to run serverless Kubernetes containers securely, reliably, and at scale Amazon EKS for AWS Fargate
  22. 22. Spare capacity with savings up to 70% off of Fargate standard pricing Improved scalability, reduced operational cost to run containers Containers New Features Accelerating momentum for AWS container services
  23. 23. Build and maintain secure OS images more quickly & easily Introducing DRAFTCompute General Availability – December 3 EC2 Image Builder
  24. 24. AWS License Manager - Simplified Windows & SQL Server BYOL New Feature DRAFTCompute General Availability – December 1 • Bring your eligible Windows and SQL BYOL Licenses to AWS • Leverage existing licensing investments to save costs • Automate ongoing management of EC2 Dedicated Hosts Simplified Management Elasticity of EC2 for Dedicated Hosts with AWS License Manager Integration (New) Windows BYOL • B A • L • A LEARN MORE WIN201 - Leadership session: Five New Features of Microsoft and .NET on AWS that you want to learn
  25. 25. Introducing DRAFTCompute General Availability – December 1 Helps customers upgrade legacy applications to run on newer, supported versions of Windows Server without any code changes Future-proof Reduced risk Cost-effective Improved security posture on supported, new OS Isolate old runtimes Compliance with industry regulations No application refactoring or recoding cost No extended support costs Decouple from underlying OS Low risk of failure on subsequent OS updates Supports all OS version Reduced operating costs AWS End-of-support Migration Program for Windows Server
  26. 26. Storage
  27. 27. EBS Direct APIs for Snapshots Introducing A simple set of APIs that provide access to directly read EBS snapshot data, enabling backup providers to achieve up to 70% faster backups for EBS volumes at lower costs. Up to 70% faster backup times More granular recovery point objectives (RPOs) Lower cost backups Storage Easily track incremental block changes on EBS volumes to achieve: General Availability – December 3
  28. 28. Amazon S3 Access Points Introducing Simplify managing data access at scale for applications using shared data sets on Amazon S3. Easily create hundreds of access points per bucket, each with a unique name and permissions customized for each application. DRAFT General Availability – December 3 Storage
  29. 29. AI & Machine Learning
  30. 30. Please fasten your seatbelts!
  31. 31. VISION SPEECH TEXT SEARCH NEW CHATBOTS PERSONALIZATION FORECASTING FRAUD NEW DEVELOPMENT NEW CONTACT CENTERS NEW Amazon SageMaker Ground Truth Augmented AI SageMaker Neo Built-in algorithms SageMaker Notebooks NEW SageMaker Experiments NEW Model tuning SageMaker Debugger NEW SageMaker Autopilot NEW Model hosting SageMaker Model Monitor NEW Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Inferentia (Inf1) FPGA Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru AI SERVICES ML SERVICES ML FRAMEWORKS & INFRASTRUCTURE Amazon Textract Amazon Kendra Contact Lens For Amazon Connect SageMaker Studio IDE NEW NEW AWS Machine Learning stack NEW
  32. 32. AI Services
  33. 33. Pre:Invent highlights https://aws.amazon.com/about-aws/whats-new/machine-learning • Amazon Comprehend: 6 new languages • Amazon Translate: 22 new languages • Amazon Transcribe: 15 new languages, alternative transcriptions • Amazon Lex: SOC compliance, sentiment analysis, web & mobile integration with Amazon Connect • Amazon Personalize: batch recommendations • Amazon Forecast: use any quantile for your predictions With region expansion across the board!
  34. 34. Introducing Amazon Transcribe Medical Easy-to-UseAccurate Affordable
  35. 35. Introducing Amazon Rekognition Custom Labels • Import images labeled by Amazon SageMaker Ground Truth… • Or label images automatically based on folder structure • Train a model on fully managed infrastructure • Split the data set for training and validation • See precision, recall, and F1 score at the end of training • Select your model • Use it with the usual Rekognition APIs
  36. 36. A2I lets you easily implement human review in machine learning workflows to improve the accuracy, speed, and scale of complex decisions. Amazon Augmented AI (A2I)
  37. 37. How Amazon Augmented AI works Client application sends input data AWS AI Service or custom ML model makes predictions Results stored to your S3 1 2 4 Low confidence predictions sent for human review 3 High-confidence predictions returned immediately to client application 5 Amazon Rekognition Amazon Textract
  38. 38. Introducing Amazon Fraud Detector A fraud detection service that makes it easy for businesses to use machine learning to detect online fraud in real-time, at scale
  39. 39. Amazon Fraud Detector – Automated Model Building 1 2 4 5 Training data in S3 63
  40. 40. Introducing Contact Lens For Amazon Connect Theme detection Built-in automatic call transcription Automated contact categorization Enhanced Contact Search Real-time sentiment dashboard and alerting Presents recurring issues based on Customer feedback Identify call types such as script compliance, competitive mentions, and cancellations. Filter calls of interest based on words spoken and customer sentiment View entire call transcript directly in Amazon Connect Quickly identify when customers are having a poor experience on live calls Easily use the power of machine learning to improve the quality of your customer experience without requiring any technical expertise
  41. 41. Introducing AWS CodeGuru Built-in code reviews with intelligent recommendations Detect and optimize expensive lines of code before production Easily identify latency and performance improvements production environment CodeGuru Reviewer CodeGuru Profiler
  42. 42. CodeGuru Reviewer: How It Works Input: Source Code Feature Extraction Machine Learning Output: Recommendations Customer provides source code as input Java AWS CodeCommit Github Extract semantic features / patterns ML algorithms identify similar code for comparison Customers see recommendations as Pull Request feedback
  43. 43. CodeGuru Example – Looping vs Waiting do { DescribeTableResult describe = ddbClient.describeTable(new DescribeTableRequest().withTableName(tableName)); String status = describe.getTable().getTableStatus(); if (TableStatus.ACTIVE.toString().equals(status)) { return describe.getTable(); } if (TableStatus.DELETING.toString().equals(status)) { throw new ResourceInUseException("Table is " + status + ", and waiting for it to become ACTIVE is not useful."); } Thread.sleep(10 * 1000); elapsedMs = System.currentTimeMillis() - startTimeMs; } while (elapsedMs / 1000.0 < waitTimeSeconds); throw new ResourceInUseException("Table did not become ACTIVE after "); This code appears to be waiting for a resource before it runs. You could use the waiters feature to help improve efficiency. Consider using TableExists, TableNotExists. For more information, see https://aws.amazon.com/blogs/developer/waiters-in-the-aws-sdk-for-java/ Recommendation Code We should use waiters instead - will help remove a lot of this code.Developer Feedback
  44. 44. CodeGuru Profiler – Example
  45. 45. LOWER COSTINCREASE IN CPU UTILIZATION AMAZON PRIME DAY 2017 VS 2018
  46. 46. Introducing Kendra Easy to find what you are looking for Fast search, and quick to set up Native connectors (S3, Sharepoint, file servers, HTTP, etc.) Natural language Queries NLU and ML core Simple API and console experiences Code samples Incremental learning through feedback Domain Expertise
  47. 47. Kendra connectors …and more coming in 2020
  48. 48. ML Services
  49. 49. Pre:Invent highlights https://aws.amazon.com/about-aws/whats-new/machine-learning • Invoke Amazon SageMaker models in Amazon Quicksight • Invoke Amazon SageMaker models in Amazon Aurora • Deploy many models on the same Amazon SageMaker endpoint
  50. 50. Fully managed infrastructure in SageMaker Introducing Amazon SageMaker Operators for Kubernetes Kubernetes customers can now train, tune, & deploy models in Amazon SageMaker
  51. 51. Machine learning is iterative involving dozens of tools and hundreds of iterations Multiple tools needed for different phases of the ML workflow Lack of an integrated experience Large number of iterations Cumbersome, lengthy processes, resulting in loss of productivity + + =
  52. 52. Introducing Amazon SageMaker Studio The first fully integrated development environment (IDE) for machine learning Organize, track, and compare thousands of experiments Easy experiment management Share scalable notebooks without tracking code dependencies Collaboration at scale Get accurate models for with full visibility & control without writing code Automatic model generation Automatically debug errors, monitor models, & maintain high quality Higher quality ML models Code, build, train, deploy, & monitor in a unified visual interface Increased productivity
  53. 53. Data science and collaboration needs to be easy Setup and manage resources Collaboration across multiple data scientists Different data science projects have different resource needs Managing notebooks and collaborating across multiple data scientists is highly complicated + + =
  54. 54. Introducing Amazon SageMaker Notebooks Access your notebooks in seconds with your corporate credentials Fast-start shareable notebooks Administrators manage access and permissions Share your notebooks as a URL with a single click Dial up or down compute resources Start your notebooks without spinning up compute resources
  55. 55. Data Processing and Model Evaluation involves a lot of operational overhead Building and scaling infrastructure for data processing workloads is complex Use of multiple tools or services implies learning and implementing new APIs All steps in the ML workflow need enhanced security, authentication and compliance Need to build and manage tooling to run large data processing and model evaluation workloads + + =
  56. 56. Introducing Amazon SageMaker Processing Analytics jobs for data processing and model evaluation Use SageMaker’s built-in containers or bring your own Bring your own script for feature engineering Custom processing Achieve distributed processing for clusters Your resources are created, configured, & terminated automatically Leverage SageMaker’s security & compliance features
  57. 57. Managing trials and experiments is cumbersome Hundreds of experiments Hundreds of parameters per experiment Compare and contrast Very cumbersome and error prone + + =
  58. 58. Introducing Amazon SageMaker Experiments Experiment tracking at scale Visualization for best results Flexibility with Python SDK & APIs Iterate quickly Track parameters & metrics across experiments & users Organize experiments Organize by teams, goals, & hypotheses Visualize & compare between experiments Log custom metrics & track models using APIs Iterate & develop high- quality models A system to organize, track, and evaluate training experiments
  59. 59. Debugging and profiling deep learning is painful Large neural networks with many layers Many connections Additional tooling for analysis and debug Extraordinarily difficult to inspect, debug, and profile the ‘black box’ + + =
  60. 60. Automatic data analysis Relevant data capture Automatic error detection Improved productivity with alerts Visual analysis and debug Introducing Amazon SageMaker Debugger Analyze and debug data with no code changes Data is automatically captured for analysis Errors are automatically detected based on rules Take corrective action based on alerts Visually analyze & debug from SageMaker Studio Analysis & debugging, explainability, and alert generation
  61. 61. Deploying a model is not the end, you need to continuously monitor it in production and iterate Concept drift due to divergence of data Model performance can change due to unknown factors Continuous monitoring of model performance and data involves a lot of effort and expense Model monitoring is cumbersome but critical + + =
  62. 62. Introducing Amazon SageMaker Model Monitor Automatic data collection Continuous Monitoring CloudWatch Integration Data is automatically collected from your endpoints Automate corrective actions based on Amazon CloudWatch alerts Continuous monitoring of models in production Visual Data analysis Define a monitoring schedule and detect changes in quality against a pre-defined baseline See monitoring results, data statistics, and violation reports in SageMaker Studio Flexibility with rules Use built-in rules to detect data drift or write your own rules for custom analysis
  63. 63. Successful ML requires complex, hard to discover combinations Largely explorative & iterative Requires broad and complete knowledge of ML domain Lack of visibility Time consuming, error prone process even for ML experts + + = of algorithms, data, parameters
  64. 64. Introducing Amazon SageMaker Autopilot Quick to start Provide your data in a tabular form & specify target prediction Automatic model creation Get ML models with feature engineering & automatic model tuning automatically done Visibility & control Get notebooks for your modelswith source code Automatic model creation with full visibility & control Recommendations & Optimization Get a leaderboard & continue to improve your model
  65. 65. Ground Truth Algorithms & Frameworks Collaborative notebooks ExperimentsDistributed Training & Debugger Deployment, Monitoring, & Hosting SageMaker AutoPilot Build, Train, Deploy Machine Learning Models Quickly at Scale Reinforcement Learning Tuning & Optimization SageMaker Studio Marketplace for ML Amazon SageMaker
  66. 66. AWS DeepRacer improvements • AWS DeepRacer Evo • Stereo camera • LIDAR sensor • New racing opportunities • Create your own races • Object Detection & Avoidance • Head-to-head racing
  67. 67. AWS DeepComposer • MIDI keyboard to experiment with music generation using ML • Compose music using Generative Adversarial Networks (GAN) • Use a pretrained model, or train your own
  68. 68. Frameworks and Infrastructure
  69. 69. Deep Graph Library https://www.dgl.ai • Python open source library that helps researchers and scientists quickly build, train, and evaluate Graph Neural Networks on their data sets • Use cases: recommendation, social networks, life sciences, cybersecurity, etc. • Available in Deep Learning Containers • PyTorch and Apache MXNet, TensorFlow coming soon • Available for training on Amazon SageMaker
  70. 70. Deep Java Library https://www.djl.ai • Java open source library, to train and deploy models • Framework agnostic • Apache MXNet for now, more will come • Train your own model, or use a pretrained one from the model zoo
  71. 71. Databases & Analytics
  72. 72. Amazon Managed Apache Cassandra Service Introducing A scalable, highly available, and serverless Apache Cassandra–compatible database service. Run your Cassandra workloads in the AWS cloud using the same Cassandra application code and developer tools that you use today. Apache Cassandra- compatible Performance at scale Highly available and secure No servers to manage DRAFTDatabases Preview – December 3 LEARN MORE DAT324: Overview of Amazon Managed Apache Cassandra Service
  73. 73. Amazon RDS Proxy Introducing Fully managed, highly available database proxy feature for Amazon RDS. Pools and shares connections to make applications more scalable, more resilient to database failures, and more secure. DRAFTDatabases Public Beta – December 3 LEARN MORE DAT368: Setting up database proxy servers with RDS Proxy
  74. 74. UltraWarm for Amazon Elasticsearch Service Introducing A low cost, scalable warm storage tier for Amazon Elasticsearch Service. Store up to 10 PB of data in a single cluster at 1/10th the cost of existing storage tiers, while still providing an interactive experience for analyzing logs. DRAFTAnalytics Public Beta – December 3 LEARN MORE ANT229: Scalable, secure, and cost-effective log analytics
  75. 75. DRAFTAnalytics Amazon Redshift RA3 instances with Managed Storage Optimize your data warehouse costs by paying for compute and storage separately General Availability – December 3 L E A R N M O R E ANT213-R1: State of the Art Cloud Data Warehousing ANT230: Amazon Redshift Reimagined: RA3 and AQUA Delivers 3x the performance of existing cloud DWs 2x performance and 2x storage as similarly priced DS2 instances (on-demand) Automatically scales your DW storage capacity Supports workloads up to 8PB (compressed) COMPUTE NODE (RA3/i3en) SSD Cache S3 STORAGE COMPUTE NODE (RA3/i3en) SSD Cache COMPUTE NODE (RA3/i3en) SSD Cache COMPUTE NODE (RA3/i3en) SSD Cache Managed storage $/node/hour $/TB/month Introducing
  76. 76. AQUA (Advanced Query Accelerator) for Amazon Redshift Introducing Redshift runs 10x faster than any other cloud data warehouse without increasing cost DRAFTAnalytics Private Beta – December 3 LEARN MORE ANT230: Amazon Redshift Reimagined: RA3 and AQUA AQUA brings compute to storage so data doesn't have to move back and forth High-speed cache on top of S3 scales out to process data in parallel across many nodes AWS designed processors accelerate data compression, encryption, and data processing 100% compatible with the current version of Redshift S3 STORAGE AQUA ADVANCED QUERY ACCELERATOR RA3 COMPUTE CLUSTER
  77. 77. Amazon Redshift Federated Query Analyze data across data warehouse, data lakes, and operational database New Feature DRAFTAnalytics Public Beta – December 3 LEARN MORE ANT213-R1: State of the Art Cloud Data Warehousing
  78. 78. Amazon Redshift Data Lake Export New Feature No other data warehouse makes it as easy to gain new insights from all your data. DRAFTAnalytics General Availability – December 3 LEARN MORE ANT335R: How to build your data analytics stack at scale with Amazon Redshift
  79. 79. DRAFTDatabases Announced – November 26 Amazon Aurora Machine Learning Integration Simple, optimized, and secure Aurora, SageMaker, and Comprehend (in preview) integration. Add ML-based predictions to databases and applications using SQL, without custom integrations, moving data around, or ML experience. New Feature
  80. 80. Amazon Athena Federated Query Analyze data across any data source via Data Source Connectors that run on AWS Lambda (SAR) New Feature DRAFTAnalytics Public preview – November 26
  81. 81. AWS Data Exchange Quickly find diverse data in one place Efficiently access 3rd-party data Easily analyze data Reach millions of AWS customers Easiest way to package and publish data products Built-in security and compliance controls For Subscribers For Providers DRAFTAnalytics Announced – November 13 L E A R N M O R E ANT238-R: AWS Data Exchange: Easily find & subscribe to third-party data in the cloud Easily find and subscribe to 3rd-party data in the cloud
  82. 82. Networking
  83. 83. Existing Service DRAFTNetworking Scale connectivity across thousands of Amazon VPCs, AWS accounts, and on-premises networks Amazon VPCAmazon VPC Amazon VPCAmazon VPC Customer gateway VPN connection AWS Direct Connect Gateway L E A R N M O R E NET203-L Leadership Session Networking AWS Transit Gateway
  84. 84. New Feature AWS Transit Gateway Inter-Region Peering General Availability – December 3 DRAFTNetworking AWS TRANSIT GATEWAY Inter-Region Peering Build global networks by connecting transit gateways across multiple AWS Regions L E A R N M O R E NET203-L Leadership Session Networking
  85. 85. High availability and improved performance of site-to-site VPN New Feature AWS Accelerated Site-to-Site VPN General Availability – December 3 DRAFTNetworking L E A R N M O R E NET203-L Leadership Session Networking
  86. 86. AWS Transit Gateway Network Manager Introducing General Availability – December 3 DRAFTNetworking L E A R N M O R E NET212 - AWS Transit Gateway Network Manager
  87. 87. New Feature Transit Gateway Multicast General Availability – December 3 DRAFTNetworking Build and deploy multicast applications in the cloud L E A R N M O R E NET203-L Leadership Session Networking
  88. 88. New Feature Amazon VPC Ingress Routing General Availability – December 3 DRAFTNetworking Route inbound and outbound traffic through a third party or AWS service L E A R N M O R E NET203-L Leadership Session Networking
  89. 89. Security
  90. 90. DRAFTManagement Tools Announced – November 21 Identify unusual (write) activity in your AWS accounts ü Save time sifting through logs ü Get ahead of issues before they impact your business AWS CloudTrail Insights Introducing • Unexpected spikes in resource provisioning • Bursts of IAM management actions • Gaps in periodic maintenance activity
  91. 91. Amazon Detective Introducing Quickly analyze, investigate, and identify the root cause of security findings and suspicious activities. Automatically distills & organizes data into a graph model Easy to use visualizations for faster & effective investigation Continuously updated as new telemetry becomes available Preview – December 3 DRAFTSecurity LEARN MORE SEC312: Introduction to Amazon Detective
  92. 92. AWS IAM Access Analyzer Introducing Continuously ensure that policies provide the intended public and cross-account access to resources, such as Amazon S3 buckets, AWS KMS keys, & AWS Identity and Access Management roles. General Availability – December 2 DRAFTSecurity Uses automated reasoning, a form of mathematical logic, to determine all possible access paths allowed by a resource policy Analyzes new or updated resource policies to help you understand potential security implications Analyzes resource policies for public or cross-account access LEARN MORE SEC309: Deep Dive into AWS IAM Access Analyzer
  93. 93. 1 Create or use existing identities, including Azure AD, and manage access centrally to multiple AWS accounts and business applications, for easy browser, command line, or mobile single sign-on access by employees. New Feature AWS Single Sign-On - Azure AD Support Announced – November 25 DRAFTSecurity
  94. 94. Extending AWS beyond the Region
  95. 95. What customers are doing with AWS IoT Remotely monitor patient health & wellness applications Manage energy resources more efficiently Enhance safety in the home, the office, and the factory floor Transform transportation with connected and autonomous vehicles Track inventory levels and manage warehouse operations Improve the performance and productivity of industrial processes Build smarter products & user experiences in homes, buildings, and cities Grow healthier crops with greater efficiencies
  96. 96. Alexa Voice Service (AVS) Integration for IoT Core New Feature DRAFTInternet of Things Announced – November 25 Quickly and cost effectively go to market with Alexa built-in capabilities on new categories of products such as light switches, thermostats, and small appliances. Accelerate time to market with certified partner development kits that work with AVS Integration for IoT Core by default. Lowers the cost of integrating Alexa Voice up to 50% by reducing the compute and memory footprint required Build new categories of Alexa Built-in products on resource constrained devices (e.g. ARM ‘M' class microcontrollers with <1MB embedded RAM).
  97. 97. Container Support for AWS IoT Greengrass New Feature DRAFTInternet of Things Announced – November 25 Deploy containers seamlessly to edge devices Move containers from the cloud to edge devices using AWS IoT Greengrass, without rewriting any code. Enables both Docker & AWS Lambda components to operate seamlessly together at the edge Use AWS IoT Greengrass Secrets Manager to manage credentials for private container registries.
  98. 98. AWS Outposts Now Available Fully managed service that extends AWS infrastructure, AWS services, APIs, and tools to virtually any connected customer site. Truly consistent hybrid experience for applications across on-premises and cloud environments. Ideal for low latency or local data processing application needs. Same AWS-designed infrastructure as in AWS regional data centers (built on AWS Nitro System) delivered to customer facilities Fully managed, monitored, and operated by AWS as in AWS Regions Single pane of management in the cloud providing the same APIs and tools as in AWS Regions Compute General Availability – December 3 LEARN MORE CMP302-R: AWS Outposts: Extend the AWS experience to on-premises environments
  99. 99. Services supported on Outposts (additionally to EC2 & EBS)
  100. 100. Local Zones Introducing Extend the AWS Cloud to more locations and closer to your end-users to support ultra low latency application use cases. Use familiar AWS services and tools and pay only for the resources you use. DRAFTCompute General Availability – December 3 The first Local Zone to be released will be located in Los Angeles.
  101. 101. AWS Wavelength Introducing Embeds AWS compute and storage inside telco providers’ 5G networks. Enables mobile app developers to deliver applications with single-digit millisecond latencies. Pay only for the resources you use. DRAFTCompute Announcement – December 3
  102. 102. AWS Wavelength Introducing Embeds AWS compute and storage inside telco providers’ 5G networks. Enables mobile app developers to deliver applications with single-digit millisecond latencies. Pay only for the resources you use. DRAFTCompute Announcement – December 3
  103. 103. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  104. 104. Here are all of the new launches! https://aws.amazon.com/new/reinvent
  105. 105. Go Build! Here to help you build.
  106. 106. Thank you. Chris Fregly Developer Advocate, AI & Machine Learning Amazon Web Services @cfregly https://aws.amazon.com/new/reinvent

×