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AWS reInvent 2022 reCap AI/ML and Data
1.
© 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
2.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
3.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
4.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. ML infrastructure and hardware
5.
© 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
6.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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 p r e : I n v e n t
7.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
8.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. ML and data governance
9.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
10.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
11.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
12.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
13.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
14.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
15.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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 p r e : I n v e n t
16.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
17.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
18.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. Preview Amazon Security Lake Automatically centralize security data into a purpose-built data lake in a few steps
19.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. Discover, analyze, and prepare data
20.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
21.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
22.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
23.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
24.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
25.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
26.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
27.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
28.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
29.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
30.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
31.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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 er 2022
32.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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")
33.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
34.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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 e c 2 0 2 2 Amazon Athena
35.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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 e c 2 0 2 2
36.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
37.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
38.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
39.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. Build and train ML models
40.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
41.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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+
42.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
43.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
44.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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.
45.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. GA Collaboration with Amazon SageMaker Studio Team-based sharing and real-time collaboration on the same notebooks
46.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
47.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
48.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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/')
49.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
50.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
51.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
52.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. Preview Amazon SageMaker – Geospatial ML Visualize predictions Select or train a model Access geospatial data sources
53.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
54.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. Deploy ML models for inference
55.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
56.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
57.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
58.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
59.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. GA Monitor Amazon SageMaker Batch Transform Jobs p r e : I n v e n t 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
60.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. Low-code / no-code ML
61.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
62.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
63.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
64.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
65.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
66.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
67.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
68.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. AI services
69.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
70.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
71.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
72.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
73.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
74.
© 2023 Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
75.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. ML education
76.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
77.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
78.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. 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
79.
© 2023, Amazon
Web Services, Inc. or its affiliates. All rights reserved. Thank you! Chris Fregly Principal Specialist Solution Architect @ AWS AI/ML