New Amazon SageMaker AI Innovations Reimagine How Customers Build and Scale Generative AI and Machine Learning Models
Amazon Web Services (AWS) announced four new innovations for Amazon SageMaker AI at AWS re:Invent, aimed at enhancing generative AI model development. The updates include three new Amazon SageMaker HyperPod capabilities and integration of AWS Partner AI applications.
Key features include: 30+ curated model training recipes for popular models like Llama and Mistral, flexible training plans for better resource management, and task governance that can reduce model development costs by up to 40%. The platform now also offers direct integration with partner applications like Comet, Deepchecks, Fiddler AI, and Lakera.
Major companies including Articul8, Commonwealth Bank of Australia, Fidelity, and Salesforce are already utilizing these new capabilities to accelerate their generative AI development.
Amazon Web Services (AWS) ha annunciato quattro nuove innovazioni per Amazon SageMaker AI durante l'AWS re:Invent, con l'obiettivo di migliorare lo sviluppo di modelli di intelligenza artificiale generativa. Gli aggiornamenti includono tre nuove capacità di Amazon SageMaker HyperPod e l'integrazione delle applicazioni AI dei partner AWS.
Le funzionalità chiave comprendono: 30+ ricette di addestramento per modelli curati per modelli popolari come Llama e Mistral, piani di addestramento flessibili per una migliore gestione delle risorse, e governance delle attività che possono ridurre i costi di sviluppo del modello fino al 40%. La piattaforma offre ora anche un'integrazione diretta con applicazioni partner come Comet, Deepchecks, Fiddler AI e Lakera.
Importanti aziende, tra cui Articul8, Commonwealth Bank of Australia, Fidelity e Salesforce, stanno già utilizzando queste nuove capacità per accelerare lo sviluppo della loro intelligenza artificiale generativa.
Amazon Web Services (AWS) anunció cuatro nuevas innovaciones para Amazon SageMaker AI en AWS re:Invent, destinadas a mejorar el desarrollo de modelos de inteligencia artificial generativa. Las actualizaciones incluyen tres nuevas capacidades de Amazon SageMaker HyperPod e integración de aplicaciones de IA de socios de AWS.
Las características clave incluyen: 30+ recetas de entrenamiento de modelos curados para modelos populares como Llama y Mistral, planes de entrenamiento flexibles para una mejor gestión de recursos, y gobernanza de tareas que pueden reducir los costos de desarrollo de modelos hasta un 40%. La plataforma ahora también ofrece integración directa con aplicaciones de socios como Comet, Deepchecks, Fiddler AI y Lakera.
Grandes empresas, incluyendo Articul8, Commonwealth Bank of Australia, Fidelity y Salesforce, ya están utilizando estas nuevas capacidades para acelerar su desarrollo de inteligencia artificial generativa.
아마존 웹 서비스(AWS)는 AWS re:Invent에서 Amazon SageMaker AI를 위한 네 가지 혁신을 발표하여 생성적 AI 모델 개발을 강화하는 데 목적을 두었습니다. 이 업데이트는 Amazon SageMaker HyperPod의 세 가지 새로운 기능과 AWS 파트너 AI 애플리케이션과의 통합을 포함합니다.
주요 기능으로는 Llama와 Mistral과 같은 인기 모델을 위한 30개 이상의 정제된 모델 학습 레시피, 더 나은 자원 관리를 위한 유연한 학습 계획, 그리고 모델 개발 비용을 최대 40%까지 줄일 수 있는 업무 거버넌스가 있습니다. 플랫폼은 이제 Comet, Deepchecks, Fiddler AI 및 Lakera와 같은 파트너 애플리케이션과의 직접 통합도 제공합니다.
Articul8, 호주 연방은행, Fidelity, Salesforce와 같은 주요 기업들이 이미 이러한 새로운 기능을 활용하여 생성적 AI 개발을 가속화하고 있습니다.
Amazon Web Services (AWS) a annoncé quatre nouvelles innovations pour Amazon SageMaker AI lors de l'AWS re:Invent, visant à améliorer le développement de modèles d'intelligence artificielle générative. Les mises à jour comprennent trois nouvelles capacités d'Amazon SageMaker HyperPod et l'intégration d'applications d'IA partenaires d'AWS.
Les fonctionnalités clés incluent : plus de 30 recettes d'entraînement de modèles soigneusement sélectionnées pour des modèles populaires comme Llama et Mistral, des plans d'entraînement flexibles pour une meilleure gestion des ressources, et la gouvernance des tâches capable de réduire les coûts de développement des modèles jusqu'à 40 %. La plateforme propose également une intégration directe avec des applications partenaires telles que Comet, Deepchecks, Fiddler AI et Lakera.
Des entreprises majeures, y compris Articul8, Commonwealth Bank of Australia, Fidelity et Salesforce, exploitent déjà ces nouvelles capacités pour accélérer leur développement en intelligence artificielle générative.
Amazon Web Services (AWS) hat auf der AWS re:Invent vier neue Innovationen für Amazon SageMaker AI angekündigt, die darauf abzielen, die Entwicklung generativer KI-Modelle zu verbessern. Zu den Updates gehören drei neue Amazon SageMaker HyperPod-Funktionen und die Integration von AWS-Partner-KI-Anwendungen.
Wichtige Funktionen sind: Über 30 kuratierte Trainingsrezepte für Modelle wie Llama und Mistral, flexible Trainingspläne für ein besseres Ressourcenmanagement und eine Aufgaben-Governance, die die Entwicklungskosten von Modellen um bis zu 40 % senken kann. Die Plattform bietet nun auch eine direkte Integration mit Partneranwendungen wie Comet, Deepchecks, Fiddler AI und Lakera.
Große Unternehmen wie Articul8, Commonwealth Bank of Australia, Fidelity und Salesforce nutzen bereits diese neuen Funktionen, um ihre Entwicklung im Bereich generativer KI zu beschleunigen.
- Integration of 30+ curated model training recipes reduces setup time from weeks to minutes
- New task governance feature can reduce model development costs by up to 40%
- Training time reduction of up to 40% with SageMaker HyperPod
- Hippocratic AI achieved 4x acceleration in model training speed using flexible training plans
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Insights
These SageMaker AI updates represent a significant technical leap in AWS's AI infrastructure offerings. The three key innovations in HyperPod - pre-built recipes, flexible training plans and task governance - address critical pain points in AI model development. The 40% cost reduction through improved resource utilization and the ability to accelerate training time are particularly noteworthy.
The addition of partner applications directly within SageMaker creates a more integrated ecosystem, streamlining the AI development workflow. This integration with tools from Comet, Deepchecks, Fiddler AI and Lakera eliminates significant technical overhead and security concerns around data movement. The pre-configured recipes for popular models like Llama and Mistral will substantially reduce the typical weeks-long optimization process to minutes.
This announcement strengthens Amazon's competitive position in the rapidly growing AI infrastructure market. By making enterprise AI development more accessible and cost-effective, AWS is positioning itself to capture a larger share of the generative AI market. The impressive customer roster including Salesforce, Thomson Reuters and major financial institutions demonstrates strong enterprise adoption.
The cost optimization features and improved resource management capabilities directly address key concerns about AI implementation costs. This could accelerate enterprise AI adoption and drive increased revenue for AWS's high-margin cloud services. The partnership ecosystem expansion also creates a stronger moat around AWS's AI platform.
Three new Amazon SageMaker HyperPod capabilities, and the addition of popular AI applications from AWS Partners directly in SageMaker, help customers remove undifferentiated heavy lifting across the AI development lifecycle, making it faster and easier to build, train, and deploy models
HyperPod AI Partner Apps in SageMaker (Graphic: Business Wire)
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Three powerful new additions to Amazon SageMaker HyperPod make it easier for customers to quickly get started with training some of today’s most popular publicly available models, save weeks of model training time with flexible training plans, and maximize compute resource utilization to reduce costs by up to
40% . - SageMaker customers can now easily and securely discover, deploy, and use fully managed generative AI and machine learning (ML) development applications from AWS partners, such as Comet, Deepchecks, Fiddler AI, and Lakera, directly in SageMaker, giving them the flexibility to choose the tools that work best for them.
- Articul8, Commonwealth Bank of Australia, Fidelity, Hippocratic AI, Luma AI, NatWest, NinjaTech AI, OpenBabylon, Perplexity, Ping Identity, Salesforce, and Thomson Reuters are among the customers using new SageMaker capabilities to accelerate generative AI model development.
“AWS launched Amazon SageMaker seven years ago to simplify the process of building, training, and deploying AI models, so organizations of all sizes could access and scale their use of AI and ML,” said Dr. Baskar Sridharan, vice president of AI/ML Services and Infrastructure at AWS. “With the rise of generative AI, SageMaker continues to innovate at a rapid pace and has already launched more than 140 capabilities since 2023 to help customers like Intuit, Perplexity, and Rocket Mortgage build foundation models faster. With today’s announcements, we’re offering customers the most performant and cost-efficient model development infrastructure possible to help them accelerate the pace at which they deploy generative AI workloads into production.”
SageMaker HyperPod: The infrastructure of choice to train generative AI models
With the advent of generative AI, the process of building, training, and deploying ML models has become significantly more difficult, requiring deep AI expertise, access to massive amounts of data, and the creation and management of large clusters of compute. Additionally, customers need to develop specialized code to distribute training across the clusters, continuously inspect and optimize their model, and manually fix hardware issues, all while trying to manage timelines and costs. This is why AWS created SageMaker HyperPod, which helps customers efficiently scale generative AI model development across thousands of AI accelerators, reducing time to train foundation models by up to
Now, even more organizations want to fine-tune popular publicly available models or train their own specialized models to transform their businesses and applications with generative AI. That is why SageMaker HyperPod continues to innovate to make it easier, faster, and more cost-efficient for customers to build, train, and deploy these models at scale with new innovations, including:
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New recipes help customers get started faster: Many customers want to take advantage of popular publicly available models, like Llama and Mistral, that can be customized to a specific use case using their organization’s data. However, it can take weeks of iterative testing to optimize training performance, including experimenting with different algorithms, carefully refining parameters, observing the impact on training, debugging issues, and benchmarking performance. To help customers get started in minutes, SageMaker HyperPod now provides access to more than 30 curated model training recipes for some of today’s most popular publicly available models, including Llama 3.2 90B, Llama 3.1 405B, and Mistral 8x22B. These recipes greatly simplify the process of getting started for customers, automatically loading training datasets, applying distributed training techniques, and configuring the system for efficient checkpointing and recovery from infrastructure failures. This empowers customers of all skill levels to achieve improved price performance for model training on AWS infrastructure from the start, eliminating weeks of iterative evaluation and testing. Customers can browse available training recipes via the SageMaker GitHub repository, adjust parameters to suit their customization needs, and deploy within minutes. Additionally, with a simple one-line edit, customers can seamlessly switch between GPU- or Trainium-based instances to further optimize price performance.
Researchers at Salesforce were looking for ways to quickly get started with foundation model training and fine-tuning, without having to worry about the infrastructure, or spending weeks optimizing their training stack for each new model. With Amazon SageMaker HyperPod recipes, they can conduct rapid prototyping when customizing foundation models. Now, Salesforce’s AI Research teams are able to get started in minutes with a variety of pre-training and fine-tuning recipes, and can operationalize foundation models with high performance.
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Flexible training plans make it easy to meet training timelines and budgets: While infrastructure innovations help drive down costs and allow customers to train models more efficiently, customers must still plan and manage the compute capacity required to complete their training tasks on time and within budget. That is why AWS is launching flexible training plans for SageMaker HyperPod. In a few clicks, customers can specify their budget, desired completion date, and maximum amount of compute resources they need. SageMaker HyperPod then automatically reserves capacity, sets up clusters, and creates model training jobs, saving teams weeks of model training time. This reduces the uncertainty customers face when trying to acquire large clusters of compute to complete model development tasks. In cases where the proposed training plan does not meet the specified time, budget, or compute requirements, SageMaker HyperPod suggests alternate plans, like extending the date range, adding more compute, or conducting the training in a different AWS Region, as the next best option. Once the plan is approved, SageMaker automatically provisions the infrastructure and runs the training jobs. SageMaker uses Amazon Elastic Compute Cloud (EC2) Capacity Blocks to reserve the right amount of accelerated compute instances needed to complete the training job in time. By efficiently pausing and resuming training jobs based on when those capacity blocks are available, SageMaker HyperPod helps make sure customers have access to the compute resources they need to complete the job on time, all without manual intervention.
Hippocratic AI develops safety-focused large language models (LLMs) for healthcare. To train several of their models, Hippocratic AI used SageMaker HyperPod flexible training plans to gain access to accelerated compute resources they needed to complete their training tasks on time. This helped them accelerate their model training speed by 4x and more efficiently scale their solution to accommodate hundreds of use cases.
Developers and data scientists at OpenBabylon, an AI company that customizes LLMs for underrepresented languages, have has been using SageMaker HyperPod flexible training plans to streamline their access to GPU resources to run large scale experiments. Using SageMaker HyperPod, they conducted 100 large scale model training experiments that allowed them to build a model that achieved state-of-the-art results in English-to-Ukrainian translation. Thanks to SageMaker HyperPod, OpenBabylon was able to achieve this breakthrough on time while effectively managing costs.
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Task governance maximizes accelerator utilization: Increasingly, organizations are provisioning large amounts of accelerated compute capacity for model training. These compute resources involved are expensive and limited, so customers need a way to govern usage to ensure their compute resources are prioritized for the most critical model development tasks, including avoiding any wastage or underutilization. Without proper controls over task prioritization and resource allocation, some projects end up stalling due to lack of resources, while others leave resources underutilized. This creates a significant burden for administrators, who must constantly re-plan resource allocation, while data scientists struggle to make progress. This prevents organizations from bringing AI innovations to market quickly and leads to cost overruns. With SageMaker HyperPod task governance, customers can maximize accelerator utilization for model training, fine-tuning, and inference, reducing model development costs by up to
40% . With a few clicks, customers can easily define priorities for different tasks and set up limits for how many compute resources each team or project can use. Once customers set limits across different teams and projects, SageMaker HyperPod will allocate the relevant resources, automatically managing the task queue to ensure the most critical work is prioritized. For example, if a customer urgently needs more compute for an inference task powering a customer-facing service, but all compute resources are in use, SageMaker HyperPod will automatically free up underutilized compute resources, or those assigned to non-urgent tasks, to make sure the urgent inference task gets the resources it needs. When this happens, SageMaker HyperPod automatically pauses the non-urgent tasks, saves the checkpoint so that all completed work is intact, and automatically resumes the task from the last-saved checkpoint once more resources are available, ensuring customers make the most of their compute.
As a fast-growing startup that helps enterprises build their own generative AI applications, Articul8 AI needs to constantly optimize its compute environment to allocate its resources as efficiently as possible. Using the new task governance capability in SageMaker HyperPod, the company has seen a significant improvement in GPU utilization, resulting in reduced idle time and accelerated end-to-end model development. The ability to automatically shift resources to high-priority tasks has increased the team's productivity, allowing them to bring new generative AI innovations to market faster.
Accelerate model development and deployment using popular AI apps from AWS Partners within SageMaker
Many customers use best-in-class generative AI and ML model development tools alongside SageMaker AI to conduct specialized tasks, like tracking and managing experiments, evaluating model quality, monitoring performance, and securing an AI application. However, integrating popular AI applications into a team’s workflow is a time-consuming, multi-step process. This includes searching for the right solution, performing security and compliance evaluations, monitoring data access across multiple tools, provisioning and managing the necessary infrastructure, building data integrations, and verifying adherence to governance requirements. Now, AWS is making it easier for customers to combine the power of specialized AI apps with the managed capabilities and security of Amazon SageMaker. This new capability removes the friction and heavy lifting for customers by making it easy to discover, deploy, and use best-in-class generative AI and ML development applications from leading partners, including Comet, Deepchecks, Fiddler, and Lakera Guard, directly within SageMaker.
SageMaker is the first service to offer a curated set of fully managed and secure partner applications for a range of generative AI and ML development tasks. This gives customers even greater flexibility and control when building, training, and deploying models, while reducing the time to onboard AI apps from months to weeks. Each partner app is fully managed by SageMaker, so customers do not have to worry about setting up the application or continuously monitoring to ensure there is enough capacity. By making these applications accessible directly within SageMaker, customers no longer need to move data out of their secure AWS environment, and they can reduce the time spent toggling between interfaces. To get started, customers simply browse the Amazon SageMaker Partner AI apps catalog, learning about the features, user experience, and pricing of the apps they want to use. They can then easily select and deploy the applications, managing access for the entire team using AWS Identity and Access Management (IAM).
Amazon SageMaker also plays a pivotal role in the development and operation of Ping Identity’s homegrown AI and ML infrastructure. With partner AI apps in SageMaker, Ping Identity will be able to deliver faster, more effective ML-powered functionality to their customers as a private, fully managed service, supporting their strict security and privacy requirements while reducing operational overhead.
All of the new SageMaker innovations are generally available to customers today.
To learn more, visit:
- The AWS Blog for details on today’s announcements: HyperPod flexible training plans, HyperPod task governance, and AI apps from partners in SageMaker.
- The Amazon SageMaker AI page to learn more about the capabilities.
- The Amazon SageMaker customer page to learn how companies are using Amazon Bedrock.
- The AWS re:Invent page for more details on everything happening at AWS re:Invent.
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