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

Transcript

1. B e o gra d Darko Meszaros Developer Advocate @darkosubotica
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. Introducing DRAFT Compute 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
20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
21. 15 new capabilities on data availability, security, protection, and cost optimization across the storage portfolio DRAFT Storage Announcement – November 20 SERVICE ANNOUNCEMENTS Amazon S3 Replication Time Control Amazon EBS Fast Snapshot Restore (FSR) Amazon FSx for Windows File Server Multi-AZ deployment option; Data Deduplication; User Quotas; Programmatic Share Management; Support for smaller file systems; Support for High Availability SQL Server deployments; Fine-grained control of in-transit encryption AWS Storage Gateway High Availability for VMware; Performance increase for Tape and File Gateway AWS DataSync 68% price reduction; Task Scheduling; Expansion to 5 additional AWS regions Amazon EFS Now available in all commercial AWS regions except China regions
22. 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
23. 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
24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
25. 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
27. 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
29. 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
31. 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. DRAFT Analytics Public Beta – December 3
32. 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
34. AQUA (Advanced Query Accelerator) for Amazon Redshift Introducing Redshift runs 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
36. Amazon Redshift Federated Query Analyze data across data warehouse, data lakes, and operational database New Feature DRAFT Analytics Public Beta – December 3 JDBC/ODBC
39. Amazon Redshift Data Lake Export New Feature No other data 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
40. 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
41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. High availability and improved performance of site-to-site VPN New Feature AWS Accelerated Site-to-Site VPN General Availability – December 3 DRAFT Networking
49. AWS Transit Gateway Network Manager Introducing General Availability – December 3 DRAFT Networking
50. New Feature Transit Gateway Multicast General Availability – December 3 DRAFT Networking Build and deploy multicast applications in the cloud
51. New Feature Amazon VPC Ingress Routing General Availability – December 3 DRAFT Networking Route inbound and outbound traffic through a third party or AWS service
52. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
53. 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
54. 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
55. 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. DRAFT App Integration General Availability – December 3
56. 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
57. 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.
58. 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.
59. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
60. 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
61. Nine new features launched across AWS IoT, including eight on IoT Day, helping customers easily scale their IoT deployments. DRAFT Internet of Things Announcements – Nov 25/Dec 1 SERVICE NEW FEATURES AWS IoT Greengrass Stream Manager Container Support AWS IoT Core Fleet Provisioning Configurable Endpoints Custom Domains for Configurable Endpoints Custom Authorizer for MQTT Connections Alexa Voice Service (AVS) Integration AWS IoT Device Management Secure Tunneling AWS IoT SiteWise SiteWise Monitor AWS IoT Day
62. 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).
63. 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.
64. 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
65. Amazon EC2 Amazon EBS Amazon ECS Amazon EKS Amazon EMR Amazon VPC Amazon RDS Amazon S3
66. Additional AWS Services Supported Locally on Outposts
67. 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. DRAFT Compute General Availability – December 3 The first Local Zone to be released will be located in Los Angeles.
68. 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
69. 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
70. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
71. 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
72. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
73. 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!
74. Introducing Amazon Transcribe Medical Easy-to-Use Accurate Affordable
76. 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
77. 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
78. Customers need + Machine Learning and humans working together
79. 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)
80. 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
81. 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
83. Fraud Detection
84. Fraud detection is difficult $$$ billions lost to fraud each year Online business prone to fraud attacks Bad actors often change tactics Changing rules = more human reviews Dependent on others to update detection logic
85. Fraud detection with ML is also difficult Top data scientists are costly & hard to find One-size-fits-all models underperform Often need to supplement data Data transformation + feature engineering Fraud imbalance = needle in a haystack
86. 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
88. Amazon Fraud Detector – Automated Model Building 1 2 4 5 Training data in S3 6 3
89. 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
92. 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
93. 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
94. 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
95. 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
96. CodeGuru Profiler – Example
97. LOWER COST INCREASE IN CPU UTILIZATION AMAZON PRIME DAY 2017 VS 2018
98. 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
99. 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
100. Kendra connectors …and more coming in 2020
102. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
103. 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
104. Fully managed infrastructure in SageMaker Introducing Amazon SageMaker Operators for Kubernetes Kubernetes customers can now train, tune, & deploy models in Amazon SageMaker
105. 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
107. 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
109. 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
110. 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
112. 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
114. 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
116. 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
118. AWS DeepRacer improvements • AWS DeepRacer Evo • Stereo camera • LIDAR sensor • New racing opportunities • Create your own races • Object Detection & Avoidance • Head-to-head racing
120. 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
122. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
123. Introducing Amazon EC2 Inferentia • Fast, low-latency inferencing at a very low cost • 64 teraOPS on 16-bit floating point (FP16 and BF16) and mixed-precision data. • 128 teraOPS on 8-bit integer (INT8) data. • Neuron SDK: https://github.com/aws/aws-neuron-sdk • Available in Deep Learning AMIs and Deep Learning Containers • TensorFlow and Apache MXNet, PyTorch coming soon Instance Name Inferentia Chips vCPUs RAM EBS Bandwidth inf1.xlarge 1 4 8 GiB Up to 3.5 Gbps inf1.2xlarge 1 8 16 GiB Up to 3.5 Gbps inf1.6xlarge 4 24 48 GiB 3.5 Gbps inf1.24xlarge 16 96 192 GiB 14 Gbps
124. 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
125. 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
126. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. https://aws.amazon.com/new/reinvent
127. Go Build! Here to help you build
128. Thank you! Darko Meszaros Developer Advocate @darkosubotica