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

Slide 1

Slide 1 text

Ско пје Darko Meszaros Senior Developer Advocate @darkosubotica

Slide 2

Slide 2 text

Slide 3

Slide 3 text

Slide 4

Slide 4 text

Slide 5

Slide 5 text

Slide 6

Slide 6 text

Agenda • Compute • Storage • Databases & Analytics • Security & Networking • Serverless & dev experience • Infra & beyond • AI & ML

Slide 7

Slide 7 text

Slide 8

Slide 8 text

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

Slide 9

Slide 9 text

AWS Graviton2 Processor Introducing Enabling the best price/performance for your cloud workloads Graviton1 Processor Graviton2 Processor DRAFT Compute Preview – December 3

Slide 10

Slide 10 text

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

Slide 11

Slide 11 text

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

Slide 12

Slide 12 text

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

Slide 13

Slide 13 text

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

Slide 14

Slide 14 text

AWS Compute Optimizer

Slide 15

Slide 15 text

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

Slide 16

Slide 16 text

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

Slide 17

Slide 17 text

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

Slide 18

Slide 18 text

Build and maintain secure OS images more quickly & easily Introducing DRAFT Compute General Availability – December 3 EC2 Image Builder

Slide 19

Slide 19 text

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

Slide 20

Slide 20 text

Slide 21

Slide 21 text

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

Slide 22

Slide 22 text

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

Slide 23

Slide 23 text

Slide 24

Slide 24 text

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

Slide 25

Slide 25 text

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

Slide 26

Slide 26 text

Slide 27

Slide 27 text

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

Slide 28

Slide 28 text

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

Slide 29

Slide 29 text

Slide 30

Slide 30 text

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

Slide 31

Slide 31 text

Slide 32

Slide 32 text

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

Slide 33

Slide 33 text

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

Slide 34

Slide 34 text

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

Slide 35

Slide 35 text

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

Slide 36

Slide 36 text

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

Slide 37

Slide 37 text

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

Slide 38

Slide 38 text

New Feature Transit Gateway Multicast General Availability – December 3 DRAFT Networking Build and deploy multicast applications in the cloud

Slide 39

Slide 39 text

New Feature Amazon VPC Ingress Routing General Availability – December 3 DRAFT Networking Route inbound and outbound traffic through a third party or AWS service

Slide 40

Slide 40 text

Slide 41

Slide 41 text

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

Slide 42

Slide 42 text

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

Slide 43

Slide 43 text

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

Slide 44

Slide 44 text

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.

Slide 45

Slide 45 text

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.

Slide 46

Slide 46 text

Slide 47

Slide 47 text

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

Slide 48

Slide 48 text

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).

Slide 49

Slide 49 text

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.

Slide 50

Slide 50 text

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

Slide 51

Slide 51 text

Amazon EC2 Amazon EBS Amazon ECS Amazon EKS Amazon EMR Amazon VPC Amazon RDS Amazon S3

Slide 52

Slide 52 text

Additional AWS Services Supported Locally on Outposts

Slide 53

Slide 53 text

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

Slide 54

Slide 54 text

Slide 55

Slide 55 text

Slide 56

Slide 56 text

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

Slide 57

Slide 57 text

Slide 58

Slide 58 text

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!

Slide 59

Slide 59 text

Introducing Amazon Transcribe Medical Easy-to-Use Accurate Affordable

Slide 60

Slide 60 text

No content

Slide 61

Slide 61 text

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

Slide 62

Slide 62 text

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

Slide 63

Slide 63 text

Customers need + Machine Learning and humans working together

Slide 64

Slide 64 text

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)

Slide 65

Slide 65 text

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

Slide 66

Slide 66 text

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

Slide 67

Slide 67 text

No content

Slide 68

Slide 68 text

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

Slide 69

Slide 69 text

No content

Slide 70

Slide 70 text

No content

Slide 71

Slide 71 text

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

Slide 72

Slide 72 text

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

Slide 73

Slide 73 text

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

Slide 74

Slide 74 text

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

Slide 75

Slide 75 text

CodeGuru Profiler – Example

Slide 76

Slide 76 text

LOWER COST INCREASE IN CPU UTILIZATION AMAZON PRIME DAY 2017 VS 2018

Slide 77

Slide 77 text

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

Slide 78

Slide 78 text

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

Slide 79

Slide 79 text

Kendra connectors …and more coming in 2020

Slide 80

Slide 80 text

No content

Slide 81

Slide 81 text

Slide 82

Slide 82 text

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

Slide 83

Slide 83 text

Fully managed infrastructure in SageMaker Introducing Amazon SageMaker Operators for Kubernetes Kubernetes customers can now train, tune, & deploy models in Amazon SageMaker

Slide 84

Slide 84 text

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

Slide 85

Slide 85 text

No content

Slide 86

Slide 86 text

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

Slide 87

Slide 87 text

No content

Slide 88

Slide 88 text

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

Slide 89

Slide 89 text

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

Slide 90

Slide 90 text

No content

Slide 91

Slide 91 text

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

Slide 92

Slide 92 text

No content

Slide 93

Slide 93 text

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

Slide 94

Slide 94 text

No content

Slide 95

Slide 95 text

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

Slide 96

Slide 96 text

No content

Slide 97

Slide 97 text

AWS DeepRacer improvements • AWS DeepRacer Evo • Stereo camera • LIDAR sensor • New racing opportunities • Create your own races • Object Detection & Avoidance • Head-to-head racing

Slide 98

Slide 98 text

No content

Slide 99

Slide 99 text

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

Slide 100

Slide 100 text

No content

Slide 101

Slide 101 text

Slide 102

Slide 102 text

No content

Slide 103

Slide 103 text

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