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
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
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
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
standard pricing Improved scalability, reduced operational cost to run containers Containers New Features Accelerating momentum for AWS container services
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
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
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
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
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
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
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. DRAFT Analytics Public Beta – December 3
10x faster than any other cloud data warehouse without increasing cost DRAFT Analytics Private Beta – December 3 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
warehouse makes it as easy to gain new insights from all your data. DRAFT Analytics General Availability – December 3 Amazon EMR Amazon Redshift Amazon Athena Amazon S3 AWS Glue
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
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
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
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
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
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
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
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
Availability – December 3 Open source libraries and toolchain that enable mobile developers to build scalable and secure cloud powered serverless applications.
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.
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
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).
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.
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
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
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
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
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
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
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
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
(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
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
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
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
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
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
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
darkomesaros
edeandrea
civitaspo
nrug_member
jacopen
ninomiya_ii
hiroramos4
moulongzhang
lycorptech_jp
kairim0
nobutomurata
tomokusaba
oikon48
szymonslowik
aleyda
marcduiker
baasie
hatefulcrawdad
sergeychernyshev
jonyablonski
techseoconnect
tammyeverts
cromwellryan
