AWS reInvent 2022 reCap AI/ML and Data

© 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved.
2022 AI/ML and Data Edition
Chris Fregly
Principal Specialist Solution
Architect @ AWS AI/ML

A P P S
D E V I C E S
P E O P L E
A P P / L O G S
T H I R D - P A R T Y D A T A
I O T / D E V I C E S
Data sources
F O R
A P P L I C A T I O N S
Amazon
Aurora
Amazon Kinesis
& Amazon MSK
F O R A N A L Y T I C S A N D
M A C H I N E L E A R N I N G
Data Lake
Amazon S3
Amazon Redshift
Data Warehouse
Amazon
Redshift
Amazon
EMR
B U S I N E S S
I N T E L L I G E N C E
Amazon
QuickSight
M A C H I N E
L E A R N I N G
Amazon
SageMaker
A N A L Y T I C S
Amazon
DynamoDB
AWS Glue
| AWS Lake Formation, Amazon DataZone
Building an end-to-end ML and data strategy

Agenda
ML infrastructure and hardware
ML and data governance
Discover, analyze, and prepare data
Build and train ML models
Deploy ML models for inference
Low-code / no-code ML
AI services
ML education

ML infrastructure and hardware

© 2023 Amazon Web Services, Inc. or its affiliates. All rights reserved.
Journey of silicon innovation at AWS
AWS Inferentia
and AWS Trainium
Machine learning acceleration
AWS Graviton
Powerful and efficient,
modern applications
AWS Nitro System
Hypervisor, Nitro Cards, network,
storage, SSD, and security

GA
Amazon SageMaker supports new instance types
SageMaker Model Training support for ml.trn1 instances
• Powered by AWS Trainium chips
• ml.trn1.2xlarge, for experimenting with a single accelerator
and training small models cost effectively
• ml.trn1.32xlarge for training large-scale models
SageMaker Inference adds eight new Graviton-based instances
• Powered by Graviton3 and Graviton2
• For Real-time and asynchronous inference model deployment options
• Graviton3: ml.c7g
• Graviton2: ml.m6g, ml.m6gd, ml.c6g,
ml.c6gd, ml.c6gn, ml.r6g, and ml.r6gd
Trn1
C7g M6g C6g R6g
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Preview
New instance types for Amazon SageMaker
P4de instance
documentation
P4de
• Provide the highest performance for ML training and HPC
applications
• Powered by 8 NVIDIA A100 GPUs with 80 GB high-performance
HBM2e GPU memory, 2X higher than the GPUs in current P4d
instances
• Up to 640GB of GPU memory, providing up to 60 percent
better ML training performance along with 20 percent lower
cost to train when compared to P4d instances
SageMaker Model Training support for ml.p4de.24xlarge instances
Dec 2022

ML and data governance

Why ML and data governance?
9
Machine learning (ML) governance
Onboard Develop Monitor
User setup
ML activities Deployment
Build
Train
Prepare
Tune
Inferences
Customers
Business
applications
Platform admin Data engineer Data scientist ML engineer ML risk officer Model approver

GA
Amazon SageMaker – New ML Governance Tools
Simplify access control and enhance transparency
Amazon SageMaker
Role Manager
Define custom user
permissions in minutes
Amazon SageMaker
Model Cards
Centralize model information
and documentation
Amazon SageMaker
Model Dashboard
Monitor model performance
in one place

GA
Amazon SageMaker Role Manager
Define custom user permissions in minutes
• Simplify permissions for
ML activities
• Use guided workflows for
role creation
• Accelerate user onboarding

GA
Amazon SageMaker Model Cards
Easily document, retrieve, and share the necessary model information
• Streamline model documentation
• Capture model information, such
as input datasets, training
environments, training results,
model purpose, performance goals
• Attach and visualize evaluation
results, such as bias and quality
metrics
• Share model cards with business
stakeholders, internal teams, or
your customers

GA
Amazon SageMaker Model Dashboard
Unified view across all your models to audit performance
• Track model behavior
• Integrates with SageMaker Model
Monitor and SageMaker Clarify
• Monitor model behavior for data
quality, model quality, bias drift,
and feature attribution drift
• Automate alerts
• Troubleshoot model deviations

Coming Soon
Share data securely with Amazon DataZone
Unlock data across organizational boundaries with build-in governance
Amazon
DataZone
Data
producers
Data
consumers
Fine-grained controls to manage and
govern access to data
Discover and share data at scale
across organizational boundaries
Makes it easy for data scientists and
other business users to discover, use,
and collaborate around that data
Manage organization-wide
governance in one place

GA
SageMaker Studio passes user permissions to EMR
Multiple SageMaker Studio users can connect to the same EMR cluster with isolated access
• All EMR jobs created from SageMaker
Studio will inherit data and resources
permissions for the given user.
• Multiple SageMaker Studio users can use the
same EMR cluster with separate access to data
• When accessing data lakes managed by Lake
Formation, table-level and column-level access
policies are enforced
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Preview
Dynamic data masking with Amazon Redshift
Protect sensitive data with role-based permissions
ID
Geo-
location Name Phone number
123 WA Ana 123-456-3568
124 NY Alice 123-457-****
125 WA Bruce 123-457-3569
126 CA Chris 123-457-****
130 CA Sharon 123-457-****
Condition
column
Mask column

Preview
Granular access with Redshift and Lake Formation
Centrally manage data sharing in Amazon Redshift with AWS Lake Formation
Amazon
Redshift
Amazon
Redshift
Amazon
Redshift
AWS
Lake Formation

Preview
Amazon Security Lake
Automatically centralize security data into a purpose-built data lake in a few steps

Discover, analyze, and prepare data

GA
Integration with many popular data sources
Supports both AWS and 3rd-party data source integrations from Amazon SageMaker
• Amazon S3, Athena, Redshift, EMR
• Salesforce, ServiceNow, Marketo
• Mailchimp, SendGrid, Zendesk, Jira, Datadog

Preview
AWS Data Exchange for AWS Lake Formation
Secure data mesh architecture with third-party data using AWS Data Exchange
AWS Lake Formation
Providers use LF-tags
to indicate which
data subscribers
should have access to
Third-party data
provider
Provider stores data in
AWS Lake Formation
AWS Data Exchange
Grants subscriber read-
only access to the data
tagged with the key-value
pairs specified by the
provider when a
subscription starts and
automatically denies
access when it ends
AWS Marketplace
Automated payments
and billing
Subscriber
Subscribers see
the data once
their account
is verified
Query provider data
Query, transform, or
share access with the
appropriate user
groups without any
upfront ETL
AWS account
+
AWS Data Exchange for AWS Lake Formation

Preview
Amazon CodeWhisperer code generator
Enterprise administrative controls, simple sign-up, and support for new languages
• Generates code recommendations based on
the comments – and prior code - in your IDE
• Available in popular IDEs such as Visual
Studio Code, JetBrains, AWS Cloud9, AWS
Lambda console
• Supports Python, Java, JavaScript, C#,
TypeScript
• Enable CodeWhisperer for your organization
with single sign-in (SSO) authentication
• Sign-up with AWS Builder ID
• Generates open source attribution
documentation for you

Preview
AWS Glue Data Quality continuous monitoring
Deliver high quality data across your data lakes and data pipelines
• Automatic data quality rule
recommendations based on your data
• Keep data quality high with ongoing data
analysis and quality checking
• Data quality for datasets in your data lake
and data pipelines
• Cost-effective to scale with pay-as-you-go
billing, with no lock-in
AWS Glue Data Quality
Amazon SageMaker

AWS Glue for Ray
Scaling your data integration workloads using Python
• AWS Glue for Ray is a new engine option on
AWS Glue.
• Data engineers and ML practitioners can use
AWS Glue for Ray to process large datasets
with Python and popular Python libraries.
• AWS Glue jobs are fire-and-forget systems
where you can submit your Ray code to the
AWS Glue jobs API
• AWS Glue for Ray facilitates the distributed
processing of your Python code over multi-
node clusters.
AWS Glue
Amazon SageMaker
Preview

GA
• Access interactive Spark clusters that start in
under a second and run faster with our
optimized runtime for Apache Spark
• Harness Apache Spark for complex, powerful
analytics using the expressive power of
Python along with its wide ecosystem
• Build Apache Spark applications without
managing resources or configuring software
using Amazon Athena
Amazon Athena
Amazon Athena for Apache Spark
Run interactive analytics on Apache Spark in under a second
Amazon SageMaker

GA
Amazon Redshift integration for Apache Spark
Build Interactive Spark Applications with Amazon SageMaker, Glue, and EMR
Redshift
Connector for
Apache Spark
Amazon Redshift
Amazon Glue
Amazon EMR
• Apache Spark applications accessing Amazon
Redshift data from AWS analytics services such as
Amazon EMR, AWS Glue, and Amazon SageMaker
• Build Apache Spark applications that read from
and write to your Amazon Redshift data
warehouse, without compromising performance or
transactional consistency.
• No manual setup and maintenance of uncertified
versions of Spark-Redshift open-source connectors
• Improved performance with only relevant data
moved from Redshift to consuming applications
Amazon SageMaker

GA
Streaming data support for Amazon Redshift
Directly ingest streaming data into your data warehouse for real-time processing
• Directly ingest streaming data into Amazon
Redshift from Kinesis Data Streams and Managed
Streaming for Apache Kafka without staging in S3
• Perform rich analytics using familiar SQL on
streaming data
• Easily create and manage extract-load-transform
(ELT) pipelines with streaming data
• Process large volumes of streaming data from
multiple sources to derive insights in seconds
Amazon
SageMaker
Amazon Kinesis
Data Streams
Amazon Managed
Streaming for
Apache Kafka
Redshift
Kinesis or Kafka
producer
KDS Stream
MSK Topic

Preview
Multi-AZ support for Amazon Redshift
Highly resistant data warehouse with auto-failover and no data loss
• Workload processing across availability zones
(AZs)
• Easy management through a single endpoint
• Auto-failover with zero data loss and no
manual intervention
Amazon Redshift
managed storage
AZ 1 AZ 2

Preview
Amazon Redshift Auto-Copy from Amazon S3
Simplified and automated file ingestion from Amazon S3 into Redshift
• Simple, low code data ingestion
• Avoid re-ingestion and manual tracking of
loaded files
• Easily convert your existing COPY statements
into automatic ingestion jobs
• Automatic ingestion of new data from
Amazon S3 based on user defined
configurations
Amazon S3
Redshift
Copy Job
Redshift
Table
Continuously
monitoring S3
folder
New file(s)
detected Ingestion
automatically
starts
Amazon
SageMaker

Preview
Zero-ETL integration from Redshift to Aurora
Access multiple Amazon Aurora databases with Amazon Redshift
• Drive holistic insights across applications
or partitions
• Analyze data from multiple Aurora
databases in the same Redshift cluster
• Leverage Redshift features such as
materialized views, data sharing and
federated access to data lakes
Amazon Redshift Amazon Aurora
Amazon
SageMaker

GA
Serverless EMR and Redshift are now GA
Pay only for the resources you use with these serverless options
Amazon EMR Serverless
• Run Spark and Hive applications without having to configure, optimize, or manage clusters
• Fine-grained auto-scaling of compute and memory resources
• Uses the performance-optimized EMR runtime
Amazon Redshift Serverless
• Run analytics queries without having to configure, tune, and manage data warehouse clusters
• Intelligently auto-scales data warehouse capacity to match your workload demand in seconds
• Supports Redshift Query Editor v2 or any business intelligence (BI) tool of your choice
Sum
m
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GA
Amazon SageMaker Data Wrangler new features
Built-in data preparation in SageMaker Studio Notebooks
• Automatically generates key
visualizations on top of Pandas
data frames
• Understand data distribution and
identify data quality issues
• Generate ML-specific insights for
ML target column
• Receive recommendations for
data transformations and code
import pandas as pd
import sagemaker_datawrangler
df = pd.read_csv("data.csv")

GA
Amazon SageMaker Data Wrangler new features
Deploy data preparation flows for real-time and batch inference
Data Wrangler
Flow
Data
Scientist
ML
Engineer
Amazon SageMaker Data Wrangler
Data
Preparation
Job
Model
Training
Inference
Pipeline
Run data
preparation
for model
training
Reuse data
transformation flow
for real-time & batch
inference
Define data
preparation
for training
Deploy
inference
• Deploy data preparation
flows from SageMaker Data
Wrangler for real-time and
batch inference
• Reuse the data
transformation flow
• Speed up your production
deployment

GA
Amazon SageMaker Data Wrangler + EMR/Athena
Now supports Amazon EMR Presto and EMR Spark as big-data query engines
• Connect to existing Amazon EMR
Presto and Athena clusters using a
visual experience in SageMaker Data
Wrangler
• Prepare data for ML in minutes using
Data Wrangler’s visual interface
• Analyze data, clean data, and create
features for ML using 300+ built-in
transformations backed by Spark
without the need to author Spark code
Amazon EMR
Presto
Amazon SageMaker
Data Wrangler
D
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2
0
2
2
Amazon Athena

GA
Apache Iceberg: Amazon SageMaker Feature Store
Now supports Apache Iceberg table format
• Create feature groups in the offline
store in Apache Iceberg table format
• Apache Iceberg is an open table
format for very large analytic
datasets
• Apache Iceberg compacts small data
files into fewer large files in the
partition, resulting in significantly
faster queries.
Amazon SageMaker
Feature Store
Apache Iceberg
D
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2
0
2
2

GA
Amazon Omics
Store, query, analyze, and generate insights from genomic and other omics data
• Built-in access control and logging
D N A R N A P R O T E I N S
Genomics Transcriptomics Proteomics

GA
Expanded API capabilities for Amazon QuickSight
Programmatic access to the underlying structure of QuickSight dashboards
• Access underlying data models of Amazon
QuickSight dashboards, reports, analyses and
templates via the AWS Software
Development Kit (SDK).
• Translate legacy BI assets to cloud-native
dashboards quickly
• Reduce migration time from months to
weeks
• Integrate into DevOps processes such as code
reviews, audits, and audit every change
before deployment
Amazon QuickSight

GA
Amazon QuickSight Paginated Reports
Day-to-day detailed operational data in custom formats
• Create, schedule, and share highly formatted
multipage reports
• Build all insights, independent of preferred
consumption model, on single source of truth
governed datasets
• Single authoring experience for dashboards
and reports
• Unified consumption experience allows users
to choose dashboards and reports
• Pay for what you use.
Amazon QuickSight

Build and train ML models

Preview
Amazon CodeWhisperer code generator
Enterprise administrative controls, simple sign-up, and support for new languages
• Generates code recommendations based on
the comments – and prior code - in your IDE
• Available in popular IDEs such as Visual
Studio Code, JetBrains, AWS Cloud9, AWS
Lambda console
• Supports Python, Java, JavaScript, C#,
TypeScript
• Enable CodeWhisperer for your organization
with single sign-in (SSO) authentication
• Sign-up with AWS Builder ID
• Generates open source attribution
documentation for you

GA
Improved Amazon SageMaker Studio usability
Redesigned user interface (UI) and user experience (UX) based on customer feedback
• Redesigned navigation menu
following ML workflow
• Dynamic landing pages with links
to videos, tutorials, blogs, etc.
• New SageMaker Home page with
one-click access to common tasks
• Redesigned launcher with quick
links to create notebook, open
console, etc.
Supported for SageMaker Studio domains running on JupyterLab 3+

GA
Notebook Jobs for Amazon SageMaker Studio
Automatic conversion of notebook code to production-ready jobs
• Run your notebooks as is or in a
parameterized fashion with just a few
simple clicks from the SageMaker
Studio
• Run notebooks on a schedule or
immediately
• No need for the end-user to modify
their existing notebook code
• View the populated notebook cells
after the job is complete, including any
visualizations
• Also available in SageMaker Studio Lab

GA
Improved Amazon SageMaker Experiments
Next-generation experiment tracking, model comparison
• New UI for easier experiment tracking
and model comparison
• Organize, track, compare and evaluate
machine learning (ML) experiments
and model versions from any IDE,
including local Jupyter notebooks
• Programmatic tracking of experiments
and logging of metrics/parameters
Dec 2022

GA
Collaboration with Amazon SageMaker Studio
Team-based sharing and real-time collaboration with shared spaces
• SageMaker Studio now supports
team-based sharing and real-time
collaboration
• Create shared spaces to access, read,
edit, and share the same notebook in
real time
• Shared EFS directory in a space
• Filtered SageMaker resources
Experiments
Model Registry
Pipelines, etc.

GA
Collaboration with Amazon SageMaker Studio
Team-based sharing and real-time collaboration on the same notebooks

Amazon SageMaker + Glue Interactive Sessions
Scaling your data integration workloads using interactive Apache Spark and Ray
• From SageMaker Studio, access AWS Glue
Interactive Sessions to access large, serverless,
distributed clusters for Apache Spark and Ray
• Data engineers and ML practitioners can use
Apache Spark or Ray to process large datasets
with Python and popular Python libraries.
• Train distributed ML models on large datasets
using algorithms from Apache Spark or Ray
AWS Glue Interactive
Sessions
Amazon SageMaker
Preview

GA
Heterogeneous clusters for Training Jobs
Reduce training cost by using a mix of instance types specific to your workload
• Use both CPU-optimized and GPU-
optimized instance types in a
single distributed training job
• Perform data transformations on
CPU instance types
• Perform ML operations on GPU
instance types
O
ct 2022

GA
Warm pools for SageMaker Training Jobs
Reduce startup time for SageMaker Training and Tuning Jobs with warm pools
• Enables faster iterations for ML
development lifecycle
• Up to 8x improvement in startup
times for SageMaker Training,
Tuning, and Autopilot jobs
• Balance cost and convenience with
configurable keep-alive times
Sept 2022
from sagemaker.pytorch import PyTorch
estimator = PyTorch(
entry_point='train.py’,
...
keep_alive_period_in_seconds=1800)
estimator.fit('s3://my_bucket/my_data/')

GA
SageMaker supports more domains and users
Now supports multiple domains within the same AWS Account
• Create multiple SageMaker
domains within the same AWS
account
• Scope access and isolate resources
to different team or business units
in your organization

GA
Amazon SageMaker tagging and cost-allocation
Automated tagging for better cost allocation and monitoring
• SageMaker automatically tags
resources at domain, user and space
level supporting detailed cost
allocation
• All SageMaker resources with an Amazon
Resource Name (ARN) including:
• Training jobs
• Processing jobs
• Kernel Gateways
• Endpoints
• etc.
• Supports more detailed cost allocation for
administrators

Preview
• Bring your own or acquire geospatial
data with just a few clicks
• Easily prepare geospatial data with
built-in operations and
transformations
• Speed model building with pre-
trained deep neural network (DNN)
models and geospatial operators
• Analyze and explore predictions with
built-in visualization tools
Amazon SageMaker – Geospatial ML
Build, train, and deploy ML models using geospatial data
Aerial and
satellite imagery
Mapping
data
Road mask
(color as speed)
24 Hour Fitness
7-Eleven
76 Gas Stations
84 Lumber
85C Bakery Cafe

Preview
Amazon SageMaker – Geospatial ML
Visualize predictions
Select or train a model
Access geospatial data sources

GA
Updates to Amazon SageMaker Pipelines
Supports local mode and cross-account artifact sharing
Fully supports “local mode” for iterative development and testing
• Fast feedback loops are critical to speed up pipeline development and testing
• Develop and test pipelines locally to reduce cost and increase developer efficiency
• Use on your laptop with any IDE such as VSCode, PyCharm, vi, or emacs
Securely share pipeline artifacts across multiple accounts
• Multi-account strategy helps achieve data, project, and team isolation
• Share pipeline artifacts between lines of business within your organization
• Supports different authorization levels including read-only and execute permissions
Fall 2022

Deploy ML models for inference

GA
Shadow testing for Amazon SageMaker Endpoints
Now supports shadow testing to compare models in real-time
• Validate performance of your models by
comparing them to production models
• SageMaker takes care of mirroring
requests
• Start small and dial up to control costs
• Catch potential configuration errors and
performance issues before they impact
end users
• Monitor progress of the shadow test and
performance metrics such as latency and
error rate through a live dashboard
Amazon SageMaker Endpoint
Production Variant
Shadow Variant
Model A
Model B
Request
Response
Request
Request
Response
Application
Response
Amazon S3
Accessible through AWS console, CLI, APIs

GA
• Set up a test for a
predefined duration
• Monitor operational
performance through a live
dashboard
• Deploy models with
confidence or rollback
Shadow testing for Amazon SageMaker Endpoints
Tools to manage shadow tests

GA
More support for large models and GPUs
Now supports deploying large models that require large volume size
• Deploy large models (up to 500GB) for inference on SageMaker’s Real-time and
Asynchronous Inference options by configuring the maximum EBS volume size and
timeout quotas
• Container health check and download timeout quotas have been made configurable
up to 60 minutes, so you have more time to download and load your model and
associated resources
Multi Model Endpoint (MME) now supports GPU-based instances
• Use MME to deploy thousands of ML models on GPU-based instances
• MME dynamically loads and unloads models from GPU memory based on incoming
traffic to the endpoint
• Save cost with MME as the GPU instances are shared by thousands of models

GA
Multi-model Endpoint (MME) support for GPUs
Use MME to deploy thousands of ML models on GPU-based instances
Run hundreds of models
behind a single endpoint to
maximize GPU utilization
Powered by NVIDIA
Triton Server to
support run models
trained on your choice
of popular frameworks
Wide selection of
up to 15 GPU
instance types
Automatically
applies endpoint
autoscaling policy to
all models
P3
G4 G5
🤗 Hugging Face

GA
Monitor Amazon SageMaker Batch Transform Jobs
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Now supported for Batch Transform Jobs in Amazon SageMaker
• Monitor the quality of ML predictions from Batch Transform jobs in SageMaker
using Amazon SageMaker Model Monitor
• SageMaker Batch Transform enables you to run predictions on datasets stored in
Amazon S3
• Collect Batch Transform data in production, analyze it, and compare it against
your training or validation data to detect deviations
• Use SageMaker Model Monitor’s built-in rules to detect drift for structured data
sets, add data transformations before you run the built-in rules, or write your own
custom rules

Low-code / no-code ML

Amazon SageMaker low-code / no-code
Business
Requirements
Data Preparation &
Feature Engineering
Model Development,
Training, and Tuning
Model Deployment
Inference & Monitoring
Autopilot
AutoML capability that automatically prepares your data, as well as builds, trains, and
tunes the best machine learning models for your tabular and time-series datasets
JumpStart
Pre-built solutions and a model zoo of pre-trained and easily tunable state-of-the-art
models for Computer Vision, and Natural Language Processing
A dedicated workspace for data engineers, data scientists and ML Ops teams to collaborate and bring ML to market faster
Data Wrangler
A faster, visual way to aggregate and
prepare data for machine learning
Canvas
A visual point-and-click interface that allows analysts to generate accurate ML predictions on their own — without
requiring any machine learning experience or having to write a single line of code.
A dedicated no-code workspace for data analysts to generate ML-powered predictions
Amazon SageMaker Studio
Amazon SageMaker Canvas
Data
Science
Teams
Business
Teams
+
Many
deployment
options
Collaboration

GA
Amazon SageMaker JumpStart ML hub
Share ML artifacts and access external hubs from Hugging Face, TensorFlow, and PyTorch
• Share ML models and notebooks with
other users in your AWS account
• Add ML artifacts developed with
SageMaker as well as those
developed outside of SageMaker
• Provides access to popular hubs
including Hugging Face, TensorFlow
Hub, and PyTorch Hub
• Browse and select shared models to
fine-tune, deploy endpoints, or run
notebooks
• Access foundational models with
billions of parameters

GA
SageMaker JumpStart foundational models
Billions of parameters and adaptable to many use cases for NLP and computer vision
AlexaTM 20B model
• Alexa Teacher Model (AlexaTM) builds large-scale, multi-task, multi-lingual models
Stable Diffusion by StabilityAI
• Stable Diffusion generates images from given text
Bloom by Hugging Face
• Bloom models complete sentences or generate long paragraphs (46 languages)
Jurassic by AI21 Labs
• Apply to virtually any language task by giving a description and a few examples
pre:Invent

GA
New Amazon SageMaker Canvas features
pre:Invent
Correlation matrices for advanced data analysis
• Correlation matrices allow you to summarize a dataset into a matrix that shows
correlations between two or more values and how they relate to one another
• Helps to identify and visualize patterns in a given dataset for advanced analysis
Encryption support with customer managed keys for time series
forecast models
• SageMaker Canvas now supports encryption at rest using CMK with AWS KMS for all
problem types currently supported by SageMaker Canvas.
Tags to track and allocate costs incurred by users
• Assign tags to user-profiles created within Amazon SageMaker to track SageMaker
Canvas usage costs categorized by users, departments, lines of businesses, or cost
centers

GA
New Amazon SageMaker Canvas features
Bring your own models (BYOM) into SageMaker Canvas to generate predictions
Bring Your Own Model (BYOM) into
SageMaker Canvas
• Register model in SageMaker Model
Registry, and share
• Share models directly from
SageMaker Autopilot and JumpStart
Improved Model Sharing and
Collaboration from SageMaker Canvas
with SageMaker Studio Users
• Share models built in SageMaker
Canvas with data scientists using
SageMaker Studio for review, update,
and feedback
Dec 2022

GA
New Amazon SageMaker Autopilot features
pre:Invent
Ensemble training mode powered by AutoGluon
• Runs multiple trials with different combinations of a subset of algorithms and
AutoGluon configuration parameters
• Uses both the best objective metrics and lowest inference latency to select the best
model candidate for an experiment
Batch inference in Amazon SageMaker Studio
• Select any of the SageMaker Autopilot models and proceed with batch inference
within SageMaker Studio
AutoML experiments are now up to 2x faster in HPO training mode
• New multi-fidelity hyperparameter optimization (HPO) strategy employs state-of-
the-art hyperband tuning algorithm on datasets that are greater than 100 MB with
100 or more trials

GA
AutoML support in Amazon SageMaker Pipelines
Launch Amazon SageMaker Autopilot jobs from SageMaker Pipelines
• SageMaker Autopilot is now integrated
with SageMaker Pipelines for automated
machine learning
• Add an automated training step
(AutoMLStep) in SageMaker Pipelines
and invoke a SageMaker Autopilot
experiment with Ensemble training
mode
Amazon SageMaker
Pipelines

AI services

GA
AWS AI Service Cards
• A new resource for Responsible AI
• Documents expected use cases,
limitations, design guidelines for
Responsible AI, and best practices
for use and operation
• Available today: Rekognition Face
Matching, Textract AnalyzeID, and
Transcribe Batch (English-US)
• Will be expanded based on customer
feedback

GA
Amazon Textract
pre:Invent
Updates to the Analyze ID API
• Data extraction for the machine readable zone, or MRZ code, on U.S. Passports.
Updates to the Analyze Expense API
• Support for 40+ normalized fields, including both summary fields such as Vendor
Address, and line item fields such as Product Code.
Updates to the forms and text extraction features
• Enhanced key-value pair extraction accuracy for single character boxed forms
commonly found in documents, such as Tax and Immigration forms.
• E13B fonts support commonly found in deposit checks/cheques
Detect signatures on any document

GA
Amazon Textract – Analyze Lending API
Amazon
Textract
Payslip
Identity
document
Bank
Statement
Extracted
Data
User
Review
Automated
Review
Approve
Reject
• Analyze and classify documents
contained in mortgage loan
applications
• Greater workflow automation to
accelerate automation efforts
• Reduce human error so that
users can focus on higher-value
tasks

GA
Amazon Transcribe – Real-Time Call Analytics
• Assist contact center agents in
resolving live calls faster
• Transcribe live calls, identify
customer experience issues and
sentiment in real time
• Combines automatic speech
NLP models trained to improve
overall customer experience

GA
Amazon Comprehend for IDP
Intelligent Document Processing
Amazon Comprehend
PDF
Microsoft
Word
Images
• Classify and extract entities
from files, without extracting
the text first
• Real-time inferencing of files,
as well as asynchronous batch
processing on large document
sets
• Combines OCR and
Comprehend NLP capabilities
to classify and extract entities

GA
Tabular search for HTML documents
Search more intuitively and effectively through tables
embedded in HTML pages
Extended language support for
semantic search
Kendra now supports semantic search for English,
Spanish, French, German, Portuguese, Japanese,
Korean, and Chinese
Credit Card Interest Rates
Bank 1 21.55
Bank 2 20.45
Bank 3 21.47
What’s the credit card with the lowest annual fees?
Credit Card Interest Rates
Bank 1 21.55
Bank 2 20.45
Bank 3 21.47
¿Qué es Amazon Kendra?
Qu'est-ce que Amazon Kendra ?
Was ist Amazon Kendra?
O que é a Amazon Kendra?
アマゾンケンドラとは?
Amazon Kendra란 무엇입니까?
什么是 Amazon Kendra？
什麼是 Amazon Kendra？
Amazon Kendra
Intelligent Enterprise Search

ML education

New fairness and bias mitigation course
• Free, public course on fairness
criteria and bias mitigation
• Taught by Amazon data scientists
who train AWS employees on ML
• 9+ hours of lectures
and hands-on exercises
>>> Get started today
Machine Learning
University

Free educator enablement program
• AI & ML educator training program
for community colleges and MSIs
nationwide
• Hands-on training sessions
• Structured curriculum
and classroom resources
• Access to an educator
community of practice
>>> Learn more
Machine Learning
University

New 3-day course for Amazon SageMaker
New 3-day classroom course available
What you’ll learn:
• Accelerate the preparation,
building, training,
deployment, and monitoring
of ML solutions by using
Amazon SageMaker Studio
• Use the tools that are part of
SageMaker Studio to
improve productivity at
every step of the ML lifecycle
>>> Find a class today
Dec 2022

Thank you!
Chris Fregly
Principal Specialist Solution Architect @
AWS AI/ML

AWS reInvent 2022 reCap AI/ML and Data

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