re:Invent 2019 - re:Cap - AWS User Group Sarajevo

Transcript

1. S a ra j e v o Darko Meszaros Senior Developer Advocate @darkosubotica Siniša Mikašinović Solutions Architect
2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
5. Agenda • Compute • Storage • Databases & Analytics • Security & Networking • Serverless & dev experience • Infra & beyond • AI & ML
6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
7. 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 Natural language processing Personalization Object detection Speech recognition Image processing Fraud detection
8. AWS Graviton2 Processor Introducing Enabling the best price/performance for your cloud workloads Graviton1 Processor Graviton2 Processor DRAFT Compute Preview – December 3
9. 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
10. Amazon Braket Introducing Fully managed service that makes it easy for scientists and developers to explore and experiment with quantum computing. DRAFT Quantum Technology Preview – December 2
11. 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 DRAFT Compute Preview – December 3
12. AWS Compute Optimizer Introducing Identify optimal Amazon EC2 instances and EC2 Auto Scaling group for your workloads using a ML-powered recommendation engine DRAFT Management Tools General Availability – December 3
13. AWS Compute Optimizer
14. 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 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
15. DRAFT Containers General Availability – December 3 Introducing The only way to run serverless Kubernetes containers securely, reliably, and at scale Amazon EKS for AWS Fargate
16. 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
17. Build and maintain secure OS images more quickly & easily Introducing DRAFT Compute General Availability – December 3 EC2 Image Builder
18. AWS License Manager - Simplified Windows & SQL Server BYOL New Feature DRAFT Compute 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
19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
20. 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
21. 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 faster backups for EBS volumes at lower costs. L E A R N M O R E CMP305-R: Amazon EBS snapshots: What’s new, best practices, and security Thursday,1:00pm, MGM Up to 70% faster backup times More granular recovery point objectives (RPOs) Lower cost backups Amazon Confidential Storage Easily track incremental block changes on EBS volumes to achieve: General Availability – December 3
22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
23. 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 DRAFT Databases Preview – December 3
24. DRAFT Databases 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
26. Amazon RDS Proxy Introducing Fully managed, highly available database proxy for Amazon RDS. Pools and shares connections to make applications more scalable, more resilient to database failures, and more secure. DRAFT Databases Public Beta – December 3
28. DRAFT Analytics Amazon Redshift RA3 instances with Managed Storage Optimize your data warehouse costs by paying for compute and storage separately General Availability – December 3 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
30. 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 DRAFT Analytics Announced – November 13 Easily find and subscribe to 3rd-party data in the cloud
31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
32. DRAFT Management Tools Announced – November 21 Identify unusual activity in your AWS accounts ü Save time sifting through logs ü Get ahead of issues before they impact your business CloudTrail Insights Introducing • Unexpected spikes in resource provisioning • Bursts of IAM management actions • Gaps in periodic maintenance activity
33. 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 DRAFT Security
34. 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 DRAFT Security 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
35. 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 DRAFT Security
36. Existing Service DRAFT Networking Scale connectivity across thousands of Amazon VPCs, AWS accounts, and on-premises networks Amazon VPC Amazon VPC Amazon VPC Amazon VPC Customer gateway VPN connection AWS Direct Connect Gateway AWS Transit Gateway
37. New Feature AWS Transit Gateway Inter-Region Peering General Availability – December 3 DRAFT Networking AWS TRANSIT GATEWAY Inter-Region Peering Build global networks by connecting transit gateways across multiple AWS Regions
38. New Feature Transit Gateway Multicast General Availability – December 3 DRAFT Networking Build and deploy multicast applications in the cloud
39. New Feature Amazon VPC Ingress Routing General Availability – December 3 DRAFT Networking Route inbound and outbound traffic through a third party or AWS service
40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
41. 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 DRAFT Serverless General Availability – December 3
42. 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 DRAFT Mobile Services Preview – December 4
43. 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. DRAFT App Integration Preview – December 3
44. Amplify for iOS & Android Introducing DRAFT Mobile Services General Availability – December 3 Open source libraries and toolchain that enable mobile developers to build scalable and secure cloud powered serverless applications.
45. Amplify DataStore New Feature DRAFT Mobile 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.
46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
47. 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
48. Alexa Voice Service (AVS) Integration for IoT Core New Feature DRAFT Internet 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).
49. Container Support for AWS IoT Greengrass New Feature DRAFT Internet 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.
50. 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
51. Amazon EC2 Amazon EBS Amazon ECS Amazon EKS Amazon EMR Amazon VPC Amazon RDS Amazon S3
52. Additional AWS Services Supported Locally on Outposts
53. 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. DRAFT Compute Announcement – December 3
54. 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. DRAFT Compute Announcement – December 3
55. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
56. 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 (Inf2) 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
57. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
58. 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!
59. Introducing Amazon Transcribe Medical Easy-to-Use Accurate Affordable
61. 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
62. Customers are forced to choose ML only systems are high speed and low cost, but do not support nuanced decision making Human only workflows offer nuanced decision making, but they’re low speed and high cost. OR
63. Customers need + Machine Learning and humans working together
64. A2I lets you easily implement human review in machine learning workflows to improve the accuracy, speed, and scale of complex decisions. Introducing Amazon Augmented AI (A2I)
65. 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
66. Human Review Workforces Amazon Mechanical Turk An on-demand 24x7 workforce of over 500,000 independent contractors worldwide, powered by Amazon Mechanical Turk Private A team of workers that you have sourced yourself, including your own employees or contractors for handling data that needs to stay within your organization Vendors A curated list of third-party vendors that specialize in providing data labeling services, available via de AWS Marketplace
68. 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
71. Introducing AWS CodeGuru Built-in code reviews with intelligent recommendations Detect and optimize expensive lines of code Identify latency and performance improvements CodeGuru Reviewer CodeGuru Profiler Write + Review Build + Test Deploy Measure Improve
72. 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
73. 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
74. CodeGuru Profiler: How It Works Input: Live application stack trace Application profile sampling Pattern matching Output: Method names, Recommendations and searchable visualizations Customer application runs in production CodeGuru Profiler continuously captures application stack trace information CodeGuru Profiler detects performance inefficiencies in the live application Customers see recommendations in their automated efficiency reports and visualizations Amazon Confidential
75. CodeGuru Profiler – Example
76. LOWER COST INCREASE IN CPU UTILIZATION AMAZON PRIME DAY 2017 VS 2018
77. Employees spend 20% of their time looking for information. —McKinsey 20% 44% 44% of the time, they cannot find the information they need to do their job. —IDC
78. 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
79. Kendra connectors …and more coming in 2020
81. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
82. 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
83. Fully managed infrastructure in SageMaker Introducing Amazon SageMaker Operators for Kubernetes Kubernetes customers can now train, tune, & deploy models in Amazon SageMaker
84. 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
86. 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
88. 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
89. 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
91. 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
93. 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
95. 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
97. AWS DeepRacer improvements • AWS DeepRacer Evo • Stereo camera • LIDAR sensor • New racing opportunities • Create your own races • Object Detection & Avoidance • Head-to-head racing
99. AWS DeepComposer • The world’s first machine learning-enabled musical keyboard • Compose music using Generative Adversarial Networks (GAN) • Use a pretrained model, or train your own
101. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
103. The Amazon Builders’ Library Architecture, software delivery, and operations By Amazon’s senior technical executives and engineers Real-world practices with detailed explanations Content available for free on the website
104. The Amazon Builders’ Library
105. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. https://aws.amazon.com/new/reinvent
106. Go Build! Here to help you build
107. Thank you! Darko Meszaros Developer Advocate @darkosubotica Siniša Mikašinović Solutions Architect