AWS Unveils the Next Generation of Amazon SageMaker, Delivering a Unified Platform for Data, Analytics, and AI
AWS has announced the next generation of Amazon SageMaker, unifying data, analytics, and AI capabilities into one integrated platform. The update introduces three major components:
1. SageMaker Unified Studio: A single environment combining SQL analytics, data processing, and ML model development tools, assisted by Amazon Q Developer.
2. SageMaker Catalog: Built on Amazon DataZone, it provides governance capabilities and access control for data and AI assets.
3. SageMaker Lakehouse: Unifies data across S3 data lakes, Redshift warehouses, and federated sources, supporting Apache Iceberg compatibility.
The platform also introduces new zero-ETL integrations with SaaS applications like Zendesk and SAP, simplifying data access for analytics and ML without complex pipelines. Companies including NatWest Group and Roche are already exploring these capabilities, with NatWest anticipating a 50% reduction in data access time and Roche expecting 40% reduction in data processing time.
AWS ha annunciato la nuova generazione di Amazon SageMaker, unificando capacità di dati, analisi e intelligenza artificiale in una piattaforma integrata. L'aggiornamento introduce tre componenti principali:
1. SageMaker Unified Studio: Un ambiente unico che combina analisi SQL, elaborazione dei dati e strumenti per lo sviluppo di modelli di ML, assistito da Amazon Q Developer.
2. SageMaker Catalog: Basato su Amazon DataZone, fornisce capacità di governance e controllo degli accessi per risorse di dati e AI.
3. SageMaker Lakehouse: Unifica i dati tra i laghi di dati S3, i magazzini Redshift e fonti federate, supportando la compatibilità con Apache Iceberg.
La piattaforma introduce anche nuove integrazioni zero-ETL con applicazioni SaaS come Zendesk e SAP, semplificando l'accesso ai dati per analisi e ML senza pipeline complesse. Aziende come NatWest Group e Roche stanno già esplorando queste capacità, con NatWest che prevede una riduzione del 50% nei tempi di accesso ai dati e Roche una riduzione del 40% nei tempi di elaborazione dei dati.
AWS ha anunciado la próxima generación de Amazon SageMaker, unificando datos, analítica y capacidades de IA en una sola plataforma integrada. La actualización presenta tres componentes principales:
1. SageMaker Unified Studio: Un entorno único que combina análisis SQL, procesamiento de datos y herramientas para el desarrollo de modelos de ML, asistido por Amazon Q Developer.
2. SageMaker Catalog: Basado en Amazon DataZone, proporciona capacidades de gobernanza y control de acceso para activos de datos e IA.
3. SageMaker Lakehouse: Unifica datos a través de lagos de datos S3, almacenes Redshift y fuentes federadas, soportando la compatibilidad con Apache Iceberg.
La plataforma también introduce nuevas integraciones zero-ETL con aplicaciones SaaS como Zendesk y SAP, simplificando el acceso a datos para análisis y ML sin pipelines complejas. Empresas como NatWest Group y Roche ya están explorando estas capacidades, con NatWest anticipando una reducción del 50% en el tiempo de acceso a datos y Roche una reducción del 40% en el tiempo de procesamiento de datos.
AWS는 데이터, 분석 및 AI 기능을 통합한 차세대 Amazon SageMaker를 발표했습니다. 이 업데이트는 세 가지 주요 구성 요소를 소개합니다:
1. SageMaker Unified Studio: SQL 분석, 데이터 처리 및 ML 모델 개발 도구를 결합한 단일 환경으로, Amazon Q Developer의 지원을 받습니다.
2. SageMaker Catalog: Amazon DataZone에 기반하여 데이터 및 AI 자산에 대한 거버넌스 기능과 액세스 제어를 제공합니다.
3. SageMaker Lakehouse: S3 데이터 레이크, Redshift 웨어하우스 및 연합 소스 간의 데이터를 통합하며 Apache Iceberg 호환성을 지원합니다.
또한 이 플랫폼은 Zendesk 및 SAP와 같은 SaaS 애플리케이션과의 새로운 제로-ETL 통합을 도입하여 복잡한 파이프라인 없이 분석 및 ML을 위한 데이터 접근을 간소화합니다. NatWest Group 및 Roche와 같은 기업들이 이미 이러한 기능을 탐색하고 있으며, NatWest는 데이터 접근 시간을 50% 줄일 것으로 예상하고 Roche는 데이터 처리 시간을 40% 줄일 것으로 기대하고 있습니다.
AWS a annoncé la prochaine génération de Amazon SageMaker, unifiant les données, l'analyse et les capacités d'IA dans une seule plateforme intégrée. La mise à jour introduit trois composants majeurs :
1. SageMaker Unified Studio: Un environnement unique combinant l'analyse SQL, le traitement des données et les outils de développement de modèles de ML, assisté par Amazon Q Developer.
2. SageMaker Catalog: Basé sur Amazon DataZone, il fournit des capacités de gouvernance et de contrôle d'accès pour les ressources de données et d'IA.
3. SageMaker Lakehouse: Unifie les données entre les lacs de données S3, les entrepôts Redshift et les sources fédérées, tout en prenant en charge la compatibilité avec Apache Iceberg.
La plateforme introduit également de nouvelles intégrations zero-ETL avec des applications SaaS telles que Zendesk et SAP, simplifiant l'accès aux données pour l'analyse et le ML sans pipelines complexes. Des entreprises comme NatWest Group et Roche explorent déjà ces fonctionnalités, NatWest s'attendant à une réduction de 50 % du temps d'accès aux données et Roche prévoyant une réduction de 40 % du temps de traitement des données.
AWS hat die nächste Generation von Amazon SageMaker angekündigt, die Daten-, Analyse- und KI-Funktionen in einer integrierten Plattform vereint. Das Update stellt drei Hauptkomponenten vor:
1. SageMaker Unified Studio: Eine einzige Umgebung, die SQL-Analysen, Datenverarbeitung und Werkzeuge zur Entwicklung von ML-Modellen kombiniert, unterstützt von Amazon Q Developer.
2. SageMaker Catalog: Basierend auf Amazon DataZone bietet es Governance-Funktionen und Zugriffskontrolle für Daten- und KI-Ressourcen.
3. SageMaker Lakehouse: Vereinheitlicht Daten über S3-Daten-Teiche, Redshift-Lager und föderierte Quellen und unterstützt die Kompatibilität mit Apache Iceberg.
Die Plattform führt auch neue Zero-ETL-Integrationen mit SaaS-Anwendungen wie Zendesk und SAP ein, um den Datenzugriff für Analysen und ML ohne komplexe Pipelines zu erleichtern. Unternehmen wie NatWest Group und Roche erkunden bereits diese Funktionen, wobei NatWest eine Reduzierung der Datenzugriffszeit um 50 % und Roche eine Reduzierung der Datenverarbeitungszeit um 40 % erwartet.
- Integration of multiple data and AI tools into a single unified platform reduces complexity
- Zero-ETL integrations with SaaS applications eliminate need for complex data pipelines
- NatWest Group expects 50% reduction in data access time
- Roche anticipates 40% reduction in data processing time
- Enhanced governance and security features through SageMaker Catalog
- SageMaker Unified Studio is still in preview phase, not yet generally available
Insights
The next-generation Amazon SageMaker represents a significant strategic evolution in AWS's AI/ML platform offering. The unified platform addresses key enterprise pain points by consolidating data analytics, ML model development and AI capabilities into a single environment. The integration of Amazon Q Developer and zero-ETL capabilities could reduce development time by 30-50% based on cited customer experiences.
The new SageMaker Lakehouse, built on Apache Iceberg, positions AWS competitively against Databricks and Snowflake in the data lakehouse market. The platform's unified governance model and built-in security features address critical enterprise requirements for AI deployment at scale. This comprehensive update strengthens AWS's market position in the rapidly growing AI infrastructure space and could accelerate enterprise AI adoption.
This platform consolidation strengthens AWS's competitive position against Microsoft Azure and Google Cloud in the enterprise AI market. The unified approach could increase customer stickiness and drive higher service adoption across AWS's ML/AI portfolio. By simplifying data access and reducing implementation complexity, AWS is positioned to capture a larger share of enterprise AI spending.
Customer testimonials from major enterprises like NatWest Group and Roche, reporting significant efficiency gains, validate the platform's value proposition. This release could drive increased revenue through higher service adoption and expanded use cases. The timing aligns with growing enterprise demand for integrated AI solutions, potentially accelerating AWS's AI-driven revenue growth in 2024.
AWS expands its widely adopted machine learning service, combining comprehensive data, analytics, and AI capabilities
- The new SageMaker Unified Studio makes it easy for customers to find and access data from across their organization and brings together purpose-built AWS analytics, machine learning (ML), and AI capabilities so customers can act on their data using the best tool for the job across all types of common data use cases, assisted by Amazon Q Developer along the way.
- SageMaker Catalog and built-in governance capabilities allow the right users to access the right data, models, and development artifacts for the right purpose.
- The new SageMaker Lakehouse unifies data across data lakes, data warehouses, operational databases, and enterprise applications, making it easy to access and work with data from within SageMaker Unified Studio and using familiar AI and ML tools or query engines compatible with Apache Iceberg.
- New zero-ETL integrations with leading Software-as-a-Service (SaaS) applications make it easy to access data from third-party SaaS applications in SageMaker Lakehouse and Amazon Redshift for analytics or ML without complex data pipelines.
- Customers and partners including Adastra, Confluent, Etleap, idealista, Informatica, Lennar, Natera, NatWest Group, NTT Data, Roche, Tableau, Toyota Motor North America, and more are already exploring the next generation of SageMaker to bring together their data, analytics, and AI initiatives.
"We are seeing a convergence of analytics and AI, with customers using data in increasingly interconnected ways—from historical analytics to ML model training and generative AI applications," said Swami Sivasubramanian, vice president of Data and AI at AWS. "To support these workloads, many customers already use combinations of our purpose-built analytics and ML tools, such as Amazon SageMaker—the de facto standard for working with data and building ML models—Amazon EMR, Amazon Redshift, Amazon S3 data lakes, and AWS Glue. The next generation of SageMaker brings together these capabilities—along with some exciting new features—to give customers all the tools they need for data processing, SQL analytics, ML model development and training, and generative AI, directly within SageMaker."
Collaborate and build faster with Amazon SageMaker Unified Studio
Today, hundreds of thousands of customers use SageMaker to build, train, and deploy ML models. Many customers also rely on the comprehensive set of purpose-built analytics services from AWS to support a wide range of workloads, including SQL analytics, search analytics, big data processing, and streaming analytics. Increasingly, customers are not using these tools in isolation; rather, they are using a combination of analytics, ML, and generative AI to derive insights and power new experiences for their users. These customers would benefit from a unified environment that brings together familiar AWS tools for analytics, ML, and generative AI, along with easy access to all of their data and the ability to easily collaborate on data projects with other members of their team or organization.
The next generation of SageMaker includes a new, unified studio that gives customers a single data and AI development environment where users can find and access all of the data in their organization, act on it using the best tool for the job across all types of common data use cases, and collaborate within teams and across roles to scale their data and AI initiatives. SageMaker Unified Studio brings together functionality and tools from the range of standalone “studios,” query editors, and visual tools that customers enjoy today in Amazon Bedrock, Amazon EMR, Amazon Redshift, AWS Glue, and the existing SageMaker Studio. This makes it easy for customers to access and use these capabilities to discover and prepare data, author queries or code, process data, and build ML models. Amazon Q Developer assists along the way to support development tasks such as data discovery, coding, SQL generation, and data integration. For example, a user could ask Amazon Q, “What data should I use to get a better idea of product sales?” or “Generate a SQL to calculate total revenue by product category.” Users can securely publish and share data, models, applications, and other artifacts with members of their team or organization, accelerating discoverability and usage of the data assets. With the Amazon Bedrock integrated development environment (IDE) in SageMaker Unified Studio, users can build and deploy generative AI applications quickly and easily using Amazon Bedrock’s selection of high-performing foundation models and tools such as Agents, Guardrails, Knowledge Bases, and Flows. SageMaker Unified Studio comes with data discovery, sharing, and governance capabilities built in, so analysts, data scientists, and engineers can easily search and find the right data they need for their use case, while applying desired security controls and permissions, maintaining access control, and securing their data.
NatWest Group, a leading bank in the
Meet enterprise security needs with Amazon SageMaker data and AI governance
The next generation of SageMaker simplifies the discovery, governance, and collaboration of data and AI across an organization. With SageMaker Catalog, built on Amazon DataZone, administrators can define and implement consistent access policies using a single permission model with granular controls, while data workers from across teams can securely discover and access approved data and models enriched with business context metadata created by generative AI. Administrators can easily define and enforce permissions across models, tools, and data sources, while customized safeguards help make AI applications secure and compliant. Customers can also safeguard their AI models with data classification, toxicity detection, guardrails, and responsible AI policies within SageMaker.
Reduce data silos and unify data with Amazon SageMaker Lakehouse
Today, more than one million data lakes are built on Amazon Simple Storage Service (Amazon S3), allowing customers to centralize their data assets and derive value with AWS analytics, AI, and ML tools. Data lakes make it possible for customers to store their data as-is—making it easy to combine data from multiple sources. Customers may have data spread across multiple data lakes, as well as a data warehouse, and would benefit from a simple way to unify all of this data.
SageMaker Lakehouse provides unified access to data stored in Amazon S3 data lakes, Redshift data warehouses, and federated data sources, reducing data silos and making it easy to query data, no matter how and where it is physically stored. With this new Apache Iceberg-compatible lakehouse capability in SageMaker, customers can access and work with all of their data from within SageMaker Unified Studio, as well as with familiar AI and ML tools and query engines compatible with Apache Iceberg open standards. Now, customers can use their preferred analytics and ML tools on their data, no matter how and where it is physically stored, to support use cases including SQL analytics, ad-hoc querying, data science, ML, and generative AI. SageMaker Lakehouse provides integrated, fine-grained access controls that are consistently applied across the data in all analytics and AI tools in the lakehouse, enabling customers to define permissions once and securely share data across their organization.
Roche, a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people's lives, will use SageMaker Lakehouse to unify data from Redshift and Amazon S3 data lakes, eliminating data silos, enhancing interoperability among teams, and allowing users to seamlessly leverage data without the need for costly data movement or duplicated security access controls. With SageMaker Lakehouse, Roche anticipates a
Quickly and easily access SaaS data with the new zero-ETL integrations with SaaS applications
To truly leverage data across their operations, businesses need seamless access to all their data, regardless of its location. That is why AWS has invested in a zero-ETL future, where data integration is no longer a tedious, manual effort, and customers can easily get their data where they need it. This includes zero-ETL integrations for Amazon Aurora MySQL and PostgreSQL, Amazon RDS for MySQL, and Amazon DynamoDB with Amazon Redshift, which help customers quickly and easily access data from popular relational and non-relational databases in Redshift and SageMaker Lakehouse for analytics and ML. In addition to operational databases and data lakes, many customers also have critical enterprise data stored in SaaS applications and would benefit from easy access to this data for analytics and ML.
The new zero-ETL integrations with SaaS applications make it easy for customers to access their data from applications such as Zendesk and SAP in SageMaker Lakehouse and Redshift for analytics and AI. This removes the need for data pipelines, which can be challenging and costly to build, complex to manage, and prone to errors that may delay access to time-sensitive insights. Zero-ETL integrations for SaaS applications incorporate best practices for full data sync, detection of incremental updates and deletes, and target merge operations.
Organizations of all sizes and across industries, including Infosys, Intuit, and Woolworths, are already benefiting from AWS zero-ETL integrations to quickly and easily connect and analyze data without building and managing data pipelines. With the zero-ETL integrations for SaaS applications, for example, online real estate platform idealista will be able to simplify their data extraction and ingestion processes, eliminating the need for multiple pipelines to access data stored in third-party SaaS applications and freeing their data engineering team to focus on deriving actionable insights from data rather than building and managing infrastructure.
The next generation of SageMaker is available today. SageMaker Unified Studio is currently in preview and will be made generally available soon.
To learn more, visit:
- The AWS Blog for details on today’s announcement.
- The Amazon SageMaker page to learn more about the service.
- The SageMaker Unified Studio page, SageMaker data and AI governance page, and SageMaker Lakehouse page to learn how companies are using these capabilities.
- The AWS re:Invent page for more details on everything happening at AWS re:Invent.
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