11/30/2022, 12:00:00 AM ~ 12/1/2022, 12:00:00 AM (UTC)

Recent Announcements

Introducing the Amazon EC2 Spot Ready Software Products

The new Amazon EC2 Spot Ready specialization helps customers identify validated AWS Partner software products that support Amazon EC2 Spot Instances, a compute purchase option that allows customers to utilize spare EC2 capacity at a discounted price from on demand (up to 90%). Amazon EC2 Spot Ready ensures that customers have a well-architected and cost-optimized solution to help them benefit from EC2 Spot savings for their workloads.

Amazon SageMaker Studio now supports real time collaboration

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables ML practitioners to perform every step of the machine learning workflow, from preparing data to building, training, tuning, and deploying models. Today, we are announcing new capabilities in SageMaker Studio to accelerate real time collaboration across ML teams.

Announcing Trusted Language Extensions for PostgreSQL on Amazon Aurora and Amazon RDS

Trusted Language Extensions for PostgreSQL is a new open source development kit to help you build high performance extensions that run safely on PostgreSQL. With Trusted Language Extensions, developers can install extensions written in a trusted language on Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service (RDS) for PostgreSQL.

Introducing Amazon Managed Streaming for Apache Kafka (MSK) Delivery Partners

Amazon Web Services (AWS) is incredibly excited to announce the new Amazon MSK Service Delivery specialization for AWS partners that help customers migrate and build real-time streaming analytics solutions with fully managed Apache Kafka. Amazon MSK provisions your servers, configures your Apache Kafka clusters, replaces servers when they fail, orchestrates server patches and upgrades, architects clusters for high availability, ensures data is durably stored and secured, sets up monitoring and alarms, and runs scaling to support load changes. With MSK Serverless, getting started with Apache Kafka is even easier. It automatically provisions and scales compute and storage resources and offers throughput-based pricing, so you can use Apache Kafka on demand and pay for the data you stream and retain.

Introducing AWS Graviton Delivery Partners

We are thrilled to announce the new AWS Graviton Delivery specialization for AWS partners that excel in enabling the best price performance for workloads in Amazon Elastic Compute Cloud (Amazon EC2). This specialization validates AWS Partners that help customers accelerate and scale their adoption of AWS Graviton to achieve better workload performance and cost savings.

AWS Machine Learning University announces educator enablement program for higher education

AWS Machine Learning University is now providing a free educator enablement program that prioritizes U.S. community colleges, Minority Serving Institutions (MSIs), and Historically Black Colleges and Universities (HBCUs). Educators can leverage these tools to launch stand-alone courses, certificates, or full degrees in data management (DM), artificial intelligence (AI), and machine learning (ML). The goal is to make early-career DM/AI/ML jobs more accessible to a broader and more diverse student population. The program offers a suite of ready-to-use tools to faculty, including a library of ready-to-teach DM/AI/ML educational materials, free computing capacity, and comprehensive faculty professional development built around MLU, Amazon’s own internal training program for ML practitioners.

Amazon AppFlow now supports over 50 Connectors

Amazon AppFlow announces the release of 22 new data connectors. With this launch, Amazon AppFlow now supports data connectivity to over 50 applications. Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications and AWS services like Amazon S3 and Amazon Redshift. As enterprises increasingly rely on SaaS services for mission-critical workflows, they face the challenge of collecting data from a growing ecosystem of services into a centralized location to derive business insights using analytics and machine learning. With Amazon AppFlow, you can easily set up data flows in minutes without writing code.

Introducing new ML governance tools for Amazon SageMaker

Today, we are excited to announce three new purpose-built tools for Amazon SageMaker to improve governance of your machine learning (ML) projects with simplified access control and enhanced transparency across your ML model’s lifecycle. With Amazon SageMaker Role Manager, you can define minimum permissions for users in minutes and onboard new users faster. SageMaker Role Manager simplifies the permission setting for ML activities and automatically generates a custom policy based on your specific needs.

Amazon GuardDuty RDS Protection now in preview

Amazon GuardDuty now offers threat detection for Amazon Aurora to identify potential threats to data stored in Aurora databases. Amazon GuardDuty RDS Protection profiles and monitors access activity to existing and new databases in your account, and uses tailored machine learning models to accurately detect suspicious logins to Aurora databases. Once a potential threat is detected, GuardDuty generates a security finding that includes database details and rich contextual information on the suspicious activity, is integrated with Aurora for direct access to database events without requiring you to modify your databases, and is designed to not affect database performance.

Introducing AWS Glue Delivery

We are excited to announce the new AWS Glue Delivery specialization, which validates AWS Partners with deep expertise and proven success delivering AWS Glue for data integration, data pipeline, and data catalogue use cases. AWS Glue is a scalable, serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. With the ability to scale on demand, AWS Glue helps customers focus on high-value activities that maximize the value of their data.

Introducing AWS AI Service Cards - a new resource for responsible AI

We are excited to announce AWS AI Service Cards, a new resource to increase transparency and help customers better understand our AWS AI services, including how to use them in a responsible way. AI service cards are a form of responsible AI documentation that provides customers with a single place to find information on the intended use cases and limitations, responsible AI design choices, and best deployment and operation practices for our AI Services. They are part of a comprehensive development process we undertake to build our services in a responsible way with fairness and bias, robustness, explainability, governance, transparency, privacy, and security in mind.

Amazon SageMaker JumpStart now enables you to more easily share ML artifacts within your organization

Amazon SageMaker JumpStart now enables you to more easily share machine learning (ML) artifacts, including notebooks and models, across your organization to accelerate model building and deployment. Amazon SageMaker JumpStart is an ML hub that accelerates your ML journey with built-in algorithms and pretrained models from popular model hubs, such as Hugging Face, and end-to-end solutions that solve common use cases.

Amazon SageMaker Data Wrangler now supports over 40 third-party applications as data sources

Today, AWS announces the general availability of Amazon SageMaker Data Wrangler support for over 40 third party applications as data sources for machine learning (ML) through the integration with Amazon AppFlow. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. Preparing high quality data for ML is often complex and time consuming as it requires aggregating data across various sources and formats using different tools. With SageMaker Data Wrangler, you can explore and import data from a variety of popular sources, such as Amazon S3, Amazon Athena, Amazon Redshift, Snowflake, Databricks and Salesforce Customer Data Platform. Starting today, we are making it easier for customers to aggregate data for ML from over 40 third-party application data sources, including Salesforce Marketing, SAP, Google Analytics, LinkedIn and more via Amazon AppFlow.

Amazon SageMaker Data Wrangler now provides built-in data preparation in notebooks

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, visualization, cleansing, and preparation from a low-code visual interface. Many ML practitioners want to explore datasets directly in notebooks to spot potential data-quality issues, like missing information, extreme values, skewed datasets, or biases, so they can correct those issues to prepare data for training ML model faster. ML practitioners can spend weeks writing boilerplate code to visualize and examine different parts of their dataset to identify and fix potential issues.

Amazon Athena now supports Apache Spark

Amazon Athena now supports Apache Spark, a popular open-source distributed processing system that is optimized for fast analytics workloads against data of any size. Athena is an interactive query service that helps you query petabytes of data wherever it lives, such as in data lakes, databases, or other data stores. With Amazon Athena for Apache Spark, you get the streamlined, interactive, serverless experience of Athena with Spark, in addition to SQL.

Amazon Redshift now supports auto-copy from Amazon S3

Amazon Redshift launches the preview of auto-copy support to simplify data loading from Amazon S3 into Amazon Redshift. You can now setup continuous file ingestion rules to track your Amazon S3 paths and automatically load new files without the need for additional tools or custom solutions.

Amazon SageMaker Studio now supports automatic conversion of notebook code to production-ready jobs

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables ML practitioners to perform every step of the machine learning workflow, from preparing data to building, training, tuning, and deploying models. Today, we’re excited to announce a new capability in SageMaker Studio notebooks that enables automatic conversion of notebook code to production-ready jobs.

Amazon SageMaker Studio launches redesigned user interface

Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single, web-based visual interface where users can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying ML models. Today, we are excited to announce a redesign that enhances the user experience by improving navigation, discoverability, and overall look and feel for SageMaker Studio.

AWS Glue announces AWS Glue Data Quality (Preview)

AWS Glue announces the preview of AWS Glue Data Quality, a new capability that automatically measures and monitors data lake and data pipeline quality. AWS Glue is a serverless, scalable data integration service that makes it more efficient to discover, prepare, move, and integrate data from multiple sources. Managing data quality is manual and time-consuming. You must set up data quality rules and validate your data against these rules on a recurring basis, also writing code to set up alerts when quality deteriorates. Analysts must manually analyze data, write rules, and then write code to implement these rules.

Amazon S3 Access Points can now be used to securely delegate access permissions for shared datasets to other AWS accounts

Amazon S3 Access Points simplify data access for any AWS service or customer application that stores data in S3 buckets. With S3 Access Points, you create unique access control policies for each access point to more easily control access to shared datasets. Now, bucket owners are able to authorize access via access points created in other accounts. In doing so, bucket owners always retain ultimate control over data access, but can delegate responsibility for more specific IAM-based access control decisions to the access point owner. This allows you to securely and easily share datasets with thousands of applications and users, and at no additional cost.

Amazon Redshift data sharing now supports centralized access control with AWS Lake formation (Preview)

Amazon Redshift data sharing enables you to efficiently share live data across Amazon Redshift data warehouses. Amazon Redshift now supports simplified governance of Amazon Redshift data sharing by enabling you to use AWS Lake Formation to centrally manage permissions on data being shared across your organization. With the new Amazon Redshift data sharing managed by AWS Lake Formation, you can view, modify, and audit permissions on the tables and views in the Redshift datashares using Lake Formation APIs and the AWS Console, and allow the Redshift datashares to be discovered and consumed by other Redshift data warehouses.

Amazon Redshift now supports Multi-AZ (Preview) for RA3 clusters

Amazon Redshift is introducing Multi-AZ deployments (Preview) that support running your data warehouse in multiple AWS Availability Zones (AZ) simultaneously and continue operating in unforeseen failure scenarios. A Multi-AZ deployment is intended for customers with business critical analytics applications that require the highest levels of availability and resiliency to AZ failures.

Amazon DocumentDB (with MongoDB compatibility) Elastic Clusters is now generally available

Amazon DocumentDB (with MongoDB compatibility) is announcing the general availability of Amazon DocumentDB Elastic Clusters, a new type of Amazon DocumentDB cluster that let’s you elastically scale your document database to handle millions of reads and writes per second with petabytes of storage.

Deploy SageMaker Data Wrangler for real-time and batch inference and additional configurations to processing jobs

Today, we are excited to announce support for deploying data preparation flows created in Data Wrangler to real-time and batch serial inference pipelines, and additional configurations for Data Wrangler processing jobs in Amazon SageMaker Data Wrangler.

Launch Amazon SageMaker Autopilot experiments from Amazon SageMaker Pipelines to easily automate MLOps workflows

Amazon SageMaker Autopilot, a low-code machine learning (ML) service which automatically builds, trains, and tunes the best ML models based on your data, is now integrated with Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for ML. This enables the automation of an end-to-end flow of building ML models using SageMaker Autopilot and integrating models into subsequent CI/CD steps.

Introducing Amazon SageMaker support for shadow testing

Amazon SageMaker supports shadow testing to help you validate performance of new machine learning (ML) models by comparing them to production models. With shadow testing, you can spot potential configuration errors and performance issues before they impact end users. SageMaker eliminates weeks of time spent building infrastructure for shadow testing, so you can release models to production faster.

Amazon SageMaker now supports geospatial ML (preview)

Amazon SageMaker now supports geospatial machine learning (ML), making it easier for data scientists and ML engineers to build, train, and deploy models using geospatial data. Today, the majority of all data generated contains geospatial information, but only a small fraction of it is used for ML because accessing, processing, and visualizing the data is complex, time consuming, and expensive.

Introducing Amazon SageMaker Ready Software Products

We are thrilled to announce the new Amazon SageMaker Ready specialization, which validates world-class AWS Partner software products that integrate with Amazon SageMaker and help customers build machine learning solutions. AWS Partners offerings in the specialization include Data Platforms, Data Pre-Processing & Feature Stores, ML Frameworks, MLOps tools, and Business Decisions & Applications. Amazon SageMaker is a fully managed machine learning (ML) service that enables data scientists and developers to quickly build, train, and deploy ML models for any use case into a production-ready hosted environment.

Amazon Kinesis Data Firehose adds support for data stream delivery to Amazon OpenSearch Serverless

Amazon Kinesis Data Firehose can now deliver streaming data to an Amazon OpenSearch Serverless. With few clicks, you can easily ingest, transform, and reliably deliver streaming data into an Amazon OpenSearch Serverless without building and managing your own data ingestion and delivery infrastructure. Kinesis Data Firehose is a fully managed service that automatically scales to match the throughput of your data and without ongoing administration.

AWS announces Amazon VPC Lattice (Preview)

Today, AWS announces the preview of Amazon VPC Lattice, an application layer networking service that makes it simple to connect, secure, and monitor service-to-service communication. You can use VPC Lattice to enable cross-account, cross-VPC connectivity, and application layer load balancing for your workloads in a consistent way regardless of the underlying compute type – instances, containers, and serverless.

Amazon QuickSight Q now supports automated data preparation

Amazon QuickSight Q now includes artificial intelligence (AI)-enhanced automated data preparation, making it fast and straightforward to augment existing dashboards for natural language questions. Preparing data for natural language query takes time and effort. Authors must imagine terms their users will input and manually replicate field names and data type information from their dashboards.

Announcing availability of AWS Outposts in Qatar, Guatemala, and Trinidad & Tobago

AWS Outposts can now be shipped and installed at your data center and on-premises locations in Qatar, Guatemala, and Trinidad & Tobago.

Announcing the preview of AWS Verified Access

Today AWS announces the preview release of AWS Verified Access, a new service that allows you to deliver secure access to corporate applications without a VPN. Built using AWS Zero Trust guiding principles, Verified Access helps you implement a work-from-anywhere model in a secure and scalable manner.

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