Infrastructure, Monday night https://www.youtube.com/watch?v=GPUWATKe15E Andy Jassy, CEO of AWS, Tuesday morning https://www.youtube.com/watch?v=7-31KgImGgU Dr. Werner Vogels, CTO of Amazon.com, Thursday morning https://www.youtube.com/watch?v=OdzaTbaQwTg
rights reserved. Service Availability https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ Service Pricing https://aws.amazon.com/pricing/
machine learning inference in the cloud Up to 16 AWS Inferentia with 128 TOPs each, first custom ML chip designed by AWS 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 Wednesday, 7pm, Aria
cloud workloads A1 Instances. 16 vCPUs,10 Gbps 3.5 Gbps EBS bandwidth 64 vCPUs, 20 Gbps 14 Gbps EBS bandwidth Graviton1 Processor Graviton2 Processor DRAFT Compute Preview – December 3 L E A R N M O R E CMP322-R: Deep dive on EC2 instances powered by AWS Graviton Wednesday 9:15am, MGM
for general purpose, compute intensive, and memory intensive workloads. l M6g C6g R6g DRAFT Built for: General-purpose. Instance storage option: M6gd Built for: Compute intensive applications. Instance storage option C6gd Built for: Memory intensive workloads. Instance storage 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 Wednesday 9:15am, MGM
proposed approach to a large-scale quantum computer. Ions, or charged atomic particles, can be confined and suspended in free space using electromagnetic fields. Qubits are stored in stable electronic states of each ion, and quantum information can be transferred through the collective quantized motion of the ions in a shared trap (interacting through the Coulomb force).
algebra and linear operators on complex vector spaces together with their dual space both in the finite- dimensional and infinite- dimensional case. It is specifically designed to ease the types of calculations that frequently come up in quantum mechanics
with quantum computing. design, test, and run quantum algorithms variety of quantum hardware technologies DRAFT Quantum Technology Preview – December 2 LEARN MORE CMP213: Introducing Quantum Computing with AWS Wednesday 11:30am, Venetian
highly sensitive data 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
of Fargate standard pricing ECS Capacity Providers Preview: Amazon ECS CLI 2.0 ECS Cluster Autoscaling Improved scalability, reduced operational cost to run containers Containers New Features Accelerating momentum for AWS container services
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 LEARN MORE CMP302-R: AWS Outposts: Extend the AWS experience to on-premises environments
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. DRAFT Compute The first Local Zone to be released will be located in Los Angeles.
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
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
of Things 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.
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 Storage
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 Up to 70% faster backup times More granular recovery point objectives (RPOs) Lower cost backups Compute Easily track incremental block changes on EBS volumes to achieve: https://aws.amazon.com/blogs/aws/new-programmatic-access-to-ebs-snapshot-content/
d,n capabilities) and P3dn support 36% higher EBS-optimized instance bandwidth, up to 19 Gbps. 33 https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-optimized.html Based on new Nitro Systems
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 LEARN MORE DAT324: Overview of Amazon Managed Apache Cassandra Service
feature 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 LEARN MORE DAT368: Setting up database proxy servers with RDS Proxy
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. Less expensive storage for older and less-frequently accessed data while still providing an interactive analytics experience. DRAFT Analytics Public Beta – December 3 LEARN MORE ANT229: Scalable, secure, and cost-effective log analytics
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 Wednesday, 10am, Venetian 2x perf of DS2, 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
10x faster than any other cloud data warehouse without increasing cost DRAFT Analytics Private Beta – December 3 LEARN MORE ANT230: Amazon Redshift Reimagined: RA3 and AQUA Wednesday, 10am, Venetian 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
lakes, and operational database New Feature DRAFT Analytics Public Beta – December 3 LEARN MORE ANT213-R1: State of the Art Cloud Data Warehousing Tuesday, 3pm, Bellagio
warehouse makes it as easy to gain new insights from all your data. format optimized for analytics Apache Parquet Amazon EMR, Amazon Athena, and Amazon SageMaker DRAFT Analytics General Availability – December 3 LEARN MORE ANT335R: How to build your data analytics stack at scale with Amazon Redshift Monday, 7pm, Venetian Tuesday, 11:30am, Aria
100s of brokers per MSK cluster Open monitoring with Prometheus Fully managed Flink applications for Kafka New Feature Announced: MSK in-place version upgrades, T Instances, CloudWatch broker logs, SASL Analytics
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 L E A R N M O R E ANT238-R: AWS Data Exchange: Easily find & subscribe to third-party data in the cloud Thursday, 2:30pm, Venetian Easily find and subscribe to 3rd-party data in the cloud
cause of security findings and suspicious activities. Automatically distills & organizes VPC, Cloud Trail, Guard Duty 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 LEARN MORE SEC312: Introduction to Amazon Detective Thursday, 1:45pm, Venetian
the intended public and cross-account access to resources, such as Amazon S3 buckets, AWS KMS keys, & AWS Identity and Access Management roles. Permissions are actually used over time? Remove unnecessary permissions. Organzitaions master account: Service last accessed data for root and OUs and accounts General Availability – December 2 DRAFT Security LEARN MORE SEC309: Deep Dive into AWS IAM Access Analyzer Thursday, 3:15pm, Venetian
VPCs, AWS accounts, and on-premises networks Amazon VPC Amazon VPC Amazon VPC Amazon VPC Customer gateway VPN connection AWS Direct Connect Gateway L E A R N M O R E NET203-L Leadership Session Networking Wednesday, 11:30am, MGM AWS Transit Gateway
December 3 DRAFT Networking Encrypt no single point of failure or bandwidth bottleneck 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 Wednesday, 11:30am, MGM
AWS Accelerated Site-to-Site VPN General Availability – December 3 DRAFT Networking L E A R N M O R E NET203-L Leadership Session Networking Wednesday, 11:30am, MGM AWS Global Accelerator to route traffic from your on-premises network to an edge location that is closest to your CGW using two static IPv4 anycast addresses
DRAFT Networking multicast applications grain control Build and deploy multicast applications in the cloud L E A R N M O R E NET203-L Leadership Session Networking Wednesday, 11:30am, MGM
- Optimizing your serverless applications Wednesday, 1:45pm, Mirage Thursday, 3:15pm, Venetian 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
APIs: low-latency, cost-effective AWS Lambda proxy and HTTP proxy APIs. 67% cost reduction, 50% latency reduction compared to REST APIs. HTTP APIs for Amazon API Gateway Introducing DRAFT Mobile 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) Wednesday 9:15am, Venetian https://docs.aws.amazon.com/apigateway/latest/developerguide/http-api-vs-rest.html $1.00/million request vs $3.50 for REST API
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. DRAFT App Integration General Availability – December 3 L E A R N M O R E API321: Event-Processing Workflows at Scale with AWS Step Functions Wednesday, 3:15pm, MGM
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 LEARN MORE CON213-L - Leadership session: Using containers and serverless to accelerate modern application development (incl schema registry demo) Wednesday 9:15am, Venetian
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 Wednesday, 11:30am, Venetian
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 Wednesday, 2:30pm, Mirage
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
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
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!
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
templates Automatic creation of custom fraud detection models Models learn from past attempts to defraud Amazon Amazon SageMaker integration One interface to review past evaluations and detection logic
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
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
= 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
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
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
Kubernetes customers can now train, tune, & deploy models in Amazon SageMaker $ kubectl apply -f training.yaml trainingjob.sagemaker.aws.amazon.com/tf-mnist created
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
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 https://aws.amazon.com/about-aws/whats-new/2019/12/introducing-the-new-amazon-sagemaker-notebook-experience-now-in-preview/
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 https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/ New Python SDK that lets data scientists and ML engineers easily run preprocessing, postprocessing and model evaluation workloads on Amazon SageMaker.
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 Python SDK for logging and analytics: Create experiments, populate them with trials, and run analytics across trials and experiments for HPO and AutoML
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 Autopilot Workshop from re:Invent: https://gitlab.com/juliensimon/aim361
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
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
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