Upgrade to Pro — share decks privately, control downloads, hide ads and more …

AWS re:Invent re:Cap 2019

Suman Debnath
December 28, 2019

AWS re:Invent re:Cap 2019

This is the content I presented at the re:Invent re:Cap 2019 events at different cities across India, Sri Lanka and Bangladesh.
Feel free to reach out to me on Twitter(@_sumand) or LinkedIN(/sumand)

Suman Debnath

December 28, 2019
Tweet

More Decks by Suman Debnath

Other Decks in Business

Transcript

  1. I n d i a , Sri L a n ka a n d B a n gla d e s h
    Suman Debnath,
    Principal Developer Advocate
    Amazon Web Services
    @_sumand

    View full-size slide

  2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
    Compute
    Storage
    Database & Analytics
    Security
    Networking
    Developers
    Regions & Availability Zones
    AI/ML
    77

    View full-size slide

  7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  8. Amazon Confidential
    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
    L E A R N M O R E CMP324-R: Deliver high performance ML inference with AWS Inferentia Wednesday, 7pm, Aria
    Natural language
    processing
    Personalization
    Object
    detection
    Speech
    recognition
    Image processing Fraud
    detection

    View full-size slide

  9. Amazon Confidential
    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

    View full-size slide

  10. Amazon Confidential
    4 Neuron Cores
    Up to 128 TOPS
    2-stage memory hierarchy
    - Large on-chip cache and commodity DRAM
    Supports FP16, BF16, INT8 data types
    Fast chip-to-chip interconnect
    TPB 61
    TPB 62
    TPB 64
    TPB 63
    Memory Memory
    Memory Memory
    TPB 5
    TPB 6
    TPB 8
    TPB 7
    Memory Memory
    Memory Memory
    AWS Inferentia quick tour
    AWS custom Built: chip, server, and software
    Neuron
    Engine
    Neuron
    Engine
    Neuron
    Engine
    Inferentia
    Neuron
    Core
    cache
    Memory
    Neuron
    Core
    cache
    Memory
    Neuron
    Core
    cache
    Memory
    Neuron
    Core
    cache
    Memory
    Compute
    General Availability – December 3

    View full-size slide

  11. Amazon Confidential
    AWS Graviton2 Processor
    Introducing
    Enabling the best price/performance for your cloud workloads
    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

    View full-size slide

  12. Amazon Confidential
    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
    L E A R N M O R E CMP322-R: Deep dive on EC2 instances powered by AWS Graviton Wednesday 9:15am, MGM

    View full-size slide

  13. Amazon Confidential
    SPEC cpu2017
    • Industry standard CPU
    intensive benchmark
    • Run on all vCPUs concurrently
    • Comparing performance/vCPU
    * All SPEC scores estimates, compiled with GCC9 -O3 -march=native,
    run on largest single socket size for each instance type tested.
    40%
    60%
    80%
    100%
    120%
    140%
    160%
    SPECint2017 Rate SPECfp2017 rate
    Performance/vCPU
    SPECcpu2017 Rate*
    M5 M6G
    DRAFT
    Compute

    View full-size slide

  14. Amazon Confidential
    SPEC jvm2008
    • Java VM benchmark
    • Run across all vCPUs concurrently
    • Comparing performance/vCPU
    * All SPEC scores estimates, run with OpenJDK11 and skipping compiler* and startup.* tests
    Tests run on largest single-socket instance size for each instance type tested.
    40%
    60%
    80%
    100%
    120%
    140%
    160%
    Performance/vCPU
    SpecJVM*
    M5 M6G
    DRAFT
    Compute

    View full-size slide

  15. Amazon Confidential
    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
    LEARN MORE CMP213: Introducing Quantum Computing with AWS Wednesday 11:30am, Venetian

    View full-size slide

  16. Amazon Confidential
    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

    View full-size slide

  17. Amazon Confidential
    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
    LEARN MORE CMP323: Optimize Performance and Cost for Your AWS Compute Wednesday, 10:45am, MGM

    View full-size slide

  18. Amazon Confidential

    View full-size slide

  19. Amazon Confidential
    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
    LEARN MORE CMP210: Dive deep on Savings Plans Wednesday, 5:30pm
    Announced – November 6
    Simplify purchasing with a flexible pricing model that offers savings of
    up to 72% on Amazon ECS and AWS Fargate
    Savings Plans

    View full-size slide

  20. Amazon Confidential
    Containers options on AWS – over time
    Docker
    Host
    AWS Cloud
    AWS managed
    Customer managed

    View full-size slide

  21. Amazon Confidential
    Containers options on AWS – over time
    Amazon ECS
    EC2 Container
    Instances
    Auto Scaling group
    2015
    ECS API
    Docker
    Host
    AWS Cloud
    AWS managed
    Customer managed

    View full-size slide

  22. Amazon Confidential
    Containers options on AWS – over time
    AWS Fargate
    Amazon ECS
    EC2 Container
    Instances
    Auto Scaling group
    2017
    ECS API
    Docker
    Host
    AWS Cloud
    AWS managed
    Customer managed

    View full-size slide

  23. Amazon Confidential
    Containers options on AWS – over time
    AWS Fargate
    Amazon ECS
    EC2 Container
    Instances
    Auto Scaling group
    Worker
    nodes
    Auto Scaling group
    DIY K8S
    ECS API
    K8s API
    Docker
    Host
    AWS Cloud
    AWS managed
    Customer managed

    View full-size slide

  24. Amazon Confidential
    Containers options on AWS – over time
    AWS Fargate
    Amazon ECS
    Amazon EKS
    EC2 Container
    Instances
    Auto Scaling group
    Worker
    nodes
    Auto Scaling group
    DIY K8S
    2018
    K8s API ECS API
    K8s API
    Docker
    Host
    AWS Cloud
    AWS managed
    Customer managed

    View full-size slide

  25. Amazon Confidential
    Containers options on AWS – over time
    AWS Fargate
    Amazon ECS
    Amazon EKS
    EC2 Container
    Instances
    Auto Scaling group
    Managed
    Node Groups
    Auto Scaling group
    Worker
    nodes
    Auto Scaling group
    DIY K8S
    2019
    K8s API ECS API
    K8s API
    Docker
    Host
    AWS Cloud
    AWS managed
    Customer managed

    View full-size slide

  26. Amazon Confidential
    Containers options on AWS – over time
    AWS Fargate
    Amazon ECS
    Amazon EKS
    EC2 Container
    Instances
    K8s API ECS API
    AWS Cloud
    Auto Scaling group
    Managed
    Node Groups
    Auto Scaling group
    Worker
    nodes
    Auto Scaling group
    DIY K8S
    NEW
    Docker
    Host
    K8s API
    AWS managed
    Customer managed

    View full-size slide

  27. Amazon Confidential
    DRAFT
    Containers
    General Availability – December 3
    LEARN MORE CON-326R - Running Kubernetes Applications on AWS Fargate
    Wednesday, 4pm, Aria
    Thursday, 1:45pm, MGM
    Introducing
    The only way to run serverless Kubernetes containers securely,
    reliably, and at scale
    Amazon EKS for AWS Fargate

    View full-size slide

  28. Amazon Confidential

    View full-size slide

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

    View full-size slide

  30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  31. Amazon Confidential
    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
    Storage
    General Availability – December 3

    View full-size slide

  32. Amazon Confidential
    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
    Compute
    Easily track incremental
    block changes on EBS
    volumes to achieve:
    General Availability – December 3

    View full-size slide

  33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  34. Amazon Confidential
    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
    LEARN MORE DAT324: Overview of Amazon Managed Apache Cassandra Service

    View full-size slide

  35. Amazon Confidential
    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.

    View full-size slide

  36. Amazon Confidential
    Amazon RDS Proxy
    Introducing
    Fully managed, highly available database proxy 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

    View full-size slide

  37. Amazon Confidential
    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
    LEARN MORE ANT229: Scalable, secure, and cost-effective log analytics

    View full-size slide

  38. Amazon Confidential
    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
    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
    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

    View full-size slide

  39. Amazon Confidential
    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
    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

    View full-size slide

  40. Amazon Confidential
    Amazon Redshift Federated Query
    Analyze data across data warehouse, data 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

    View full-size slide

  41. Amazon Confidential
    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
    LEARN MORE
    ANT335R: How to build your data analytics stack at scale with Amazon
    Redshift
    Monday, 7pm, Venetian
    Tuesday, 11:30am, Aria

    View full-size slide

  42. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  43. Amazon Confidential
    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
    LEARN MORE SEC312: Introduction to Amazon Detective Thursday, 1:45pm, Venetian

    View full-size slide

  44. Amazon Confidential
    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
    LEARN MORE SEC309: Deep Dive into AWS IAM Access Analyzer Thursday, 3:15pm, Venetian

    View full-size slide

  45. Amazon Confidential
    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
    L E A R N M O R E NET203-L Leadership Session Networking Wednesday, 11:30am, MGM

    View full-size slide

  46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  47. Amazon Confidential
    L E A R N M O R E SVS401 - 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
    DRAFT
    Serverless
    General Availability – December 3

    View full-size slide

  48. Amazon Confidential
    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
    L E A R N M O R E API321: Event-Processing Workflows at Scale with AWS Step Functions Wednesday, 3:15pm, MGM

    View full-size slide

  49. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  50. Amazon Confidential
    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
    LEARN MORE
    CMP302-R: AWS Outposts: Extend the AWS experience to on-premises
    environments
    Wednesday at 11:30am, Aria
    Thursday at 3:15pm, Mirage
    Friday at 10:45am, Mirage

    View full-size slide

  51. Amazon Confidential

    View full-size slide

  52. Amazon Confidential
    Additional AWS Services Supported Locally on Outposts

    View full-size slide

  53. Amazon Confidential
    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.

    View full-size slide

  54. Amazon Confidential
    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

    View full-size slide

  55. Amazon Confidential
    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

    View full-size slide

  56. Amazon Confidential
    71

    View full-size slide

  57. Amazon Confidential
    72

    View full-size slide

  58. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
    AI & Machine Learning Launches

    View full-size slide

  59. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  60. 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!

    View full-size slide

  61. Speech Recognition
    For Healthcare

    View full-size slide

  62. Introducing Amazon Transcribe Medical
    Easy-to-Use
    Accurate Affordable

    View full-size slide

  63. Custom Image Models

    View full-size slide

  64. 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

    View full-size slide

  65. Fraud Detection

    View full-size slide

  66. 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

    View full-size slide

  67. 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

    View full-size slide

  68. 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

    View full-size slide

  69. Amazon Fraud Detector – Key Features
    Pre-built fraud
    detection model
    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

    View full-size slide

  70. Amazon Fraud Detector – Automated Model Building
    1 2 4 5
    Training
    data in S3
    6
    3

    View full-size slide

  71. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  72. Speech & Text Analytics
    For Contact Centers

    View full-size slide

  73. Challenges in contact centers
    • Better visibility into quality of customer interactions
    • Cost prohibitive

    • Timely discovery of emerging issues
    • Support for live calls
    • End user experience

    View full-size slide

  74. 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

    View full-size slide

  75. Improving code quality

    View full-size slide

  76. Typical Application Build and Run Process
    Write +
    Review
    Build +
    Test
    Deploy Measure Improve
    1. Code Reviews require expertise in multiple areas such as
    knowledge of AWS APIs, Concurrency, etc.
    2. Code analyzer tools require high accuracy.
    3. Distributed Cloud application are difficult to optimize.
    4. Performance engineering expertise is hard to find.

    View full-size slide

  77. Introducing AWS CodeGuru
    Built-in code reviews
    with intelligent
    recommendations
    Detect and optimize
    expensive lines of
    code before
    production
    Easily identify latency
    and performance
    improvements
    production
    environment
    CodeGuru Reviewer CodeGuru Profiler

    View full-size slide

  78. 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

    View full-size slide

  79. Enterprise Search

    View full-size slide

  80. 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

    View full-size slide

  81. 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

    View full-size slide

  82. Kendra connectors
    …and more coming in 2020

    View full-size slide

  83. Getting started with Kendra
    Step 1
    Create an index
    An index is the place where
    you add your data sources
    to make them searchable
    in Kendra.
    Step 2
    Add data sources
    Add and sync your data
    from S3, Sharepoint, Box
    and other data sources, to
    your index.
    Step 3
    Test & deploy
    After syncing your data,
    visit the Search console
    page to test search &
    deploy Kendra in your
    search application.

    View full-size slide

  84. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

    View full-size slide

  85. 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

    View full-size slide

  86. Machine learning is iterative involving
    dozens of tools and hundreds of
    iterations
    Multiple tools needed for
    different phases of the
    ML workflow
    Lack of an integrated
    experience
    Large number of iterations
    Cumbersome, lengthy processes, resulting in
    loss of productivity
    +
    +
    =

    View full-size slide

  87. 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

    View full-size slide

  88. Data science and collaboration
    needs to be easy
    Setup and manage resources
    Collaboration across
    multiple data scientists
    Different data science
    projects have different
    resource needs
    Managing notebooks and
    collaborating across
    multiple data scientists is
    highly complicated
    +
    +
    =

    View full-size slide

  89. 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

    View full-size slide

  90. 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

    View full-size slide

  91. Managing trials and experiments is
    cumbersome
    Hundreds of experiments
    Hundreds of parameters
    per experiment
    Compare and contrast
    Very cumbersome and
    error prone
    +
    +
    =

    View full-size slide

  92. 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

    View full-size slide

  93. Debugging and profiling
    deep learning is painful
    Large neural networks
    with many layers
    Many connections
    Additional tooling for analysis
    and debug
    Extraordinarily difficult
    to inspect, debug, and profile
    the ‘black box’
    +
    +
    =

    View full-size slide

  94. 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

    View full-size slide

  95. Introducing Amazon SageMaker Autopilot
    Quick to start
    Provide your data in a
    tabular form & specify target
    prediction
    Automatic
    model creation
    Get ML models with feature
    engineering & automatic model
    tuning automatically done
    Visibility & control
    Get notebooks for your
    modelswith source code
    Automatic model creation with full visibility & control
    Recommendations &
    Optimization
    Get a leaderboard & continue
    to improve your model

    View full-size slide

  96. Ground
    Truth
    Algorithms
    & Frameworks
    Collaborative
    notebooks
    Experiments
    Distributed
    Training &
    Debugger
    Deployment,
    Monitoring, & Hosting
    SageMaker AutoPilot
    Build, Train, Deploy Machine Learning Models Quickly at Scale
    Reinforcement
    Learning
    Tuning
    & Optimization
    SageMaker Studio
    Marketplace
    for ML
    Amazon SageMaker

    View full-size slide

  97. 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

    View full-size slide

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

    View full-size slide

  99. AWS DeepComposer
    • The world’s first machine
    learning-enabled musical
    keyboard
    • Compose music using Generative
    Adversarial Networks (GAN)
    • Use a pretrained model, or train
    your own

    View full-size slide

  100. Enjoy!
    Build cool stuff,
    and please send us feedback!

    View full-size slide