AWS Unveils Next-Generation SageMaker: A Unified Platform for Data, Analytics, and AI

Sagemaker AI reimagines data management and AIOps - SiliconANGLE

AWS reimagines AI lifecycle management with Unified Studio and HyperPod Since 2017, Amazon SageMaker has empowered organizations to harness machine learning for diverse applications. Initially a tool for data scientists, its utility has expanded to include MLOps engineers, data engineers and business stakeholders. The SageMaker AI rebrand underscores its evolution into a comprehensive platform integrating data management and AI development. "A few years ago, machine learning was mostly a data scientist's pursuit, and data scientists were taking data within organizations and building machine learning models," said Ankur Mehrotra (pictured), director and general manager of Amazon SageMaker at Amazon Web Services Inc. "Over the years, we saw more personas getting involved. We saw MLOps engineers getting involved to put those models in production. We then saw data engineers get involved to help data scientists prepare data to build these models. Then we saw business stakeholders involved in the decision-making process, etc." Mehrotra spoke with theCUBE Research's Dave Vellante and John Furrier for theCUBE's "Cloud AWS re:Invent Coverage," during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed SageMaker AI equipping organizations with the tools to innovate faster and at scale by addressing infrastructure, governance and ease of use. At the heart of the transformation is SageMaker Unified Studio, a unified interface that seamlessly combines data preparation, machine learning model development and governance. This integration allows teams to collaborate more efficiently, leveraging a shared context across workflows. Unified Studio ensures that businesses no longer juggle disparate tools, streamlining the AI lifecycle under one umbrella, according to Mehrotra. "SageMaker manages those tasks on your behalf, and that's why it's a managed service," he said. "For example, if you were to build a model or deploy a model, then SageMaker AI would now provide the infrastructure, set up the tools, take your data and run the job to do that task." HyperPod, a purpose-built feature for gen AI, addresses the challenges of scaling GPU and Trainium clusters. With capabilities such as automatic fault tolerance and self-healing environments, it ensures that infrastructure issues do not derail projects. The introduction of flexible training plans, leveraging EC2 capacity blocks, enables customers to secure and manage compute resources efficiently, minimizing downtime and maximizing productivity, Mehrotra added. "Last re:Invent, we announced SageMaker HyperPod, which is a purpose-built capability for generative AI model development," he said. "In HyperPod, you can basically easily set up a GPU or a Trainium cluster and you can easily scale up your cluster and manage the cluster with familiar tools. Also, SageMaker takes care of automatically resolving any health issues within the cluster and provides a self-healing cluster environment and also improves the performance of your training, fine-tuning jobs within that environment." To reduce experimentation times, SageMaker AI also has HyperPod recipes, which are pre-optimized configurations for popular model architectures, such as Llama and Mistral. These recipes handle parameter optimization, checkpointing and fine-tuning, enabling users to initiate generative AI projects within minutes instead of weeks, according to Mehrotra.

AWS transforms Amazon SageMaker into a single platform for AI and data analytics - SiliconANGLE

AWS transforms Amazon SageMaker into a single platform for AI and data analytics Amazon Web Services Inc.'s popular neural network development platform Amazon SageMaker is getting a major refresh, with a host of new capabilities that will support the integration of faster structured query language analytics, petabyte-scale data processing and more besides. The new updates, announced at the annual AWS extravaganza re:Invent, are designed to transform Amazon SageMaker into a more comprehensive, fully integrated platform for artificial intelligence development. They include the new SageMaker Unified Studio, which is a portal through which customers can access data from across their organization, along with various AI and machine learning development tools, and the SageMaker Catalog, which hosts a collection of powerful large language models and other developmental artifacts. Meanwhile, SageMaker is getting its very own data platform, called SageMaker Lakehouse, which unifies data from multiple data lakes, warehouses and operational databases and applications, making it easier for developers to access. The company launched Amazon SageMaker back in 2017, long before the current AI development craze that was inspired by OpenAI's ChatGPT, and it has since become the cloud infrastructure giant's primary AI application development platform. It hosts a glut of tools for building AI applications, making it possible to create neural networks, train them, deploy and monitor their performance, fine-tune them and perform various other essential tasks in one place SageMaker Unified Studio is available in preview now and represents a major evolution of the platform, giving users access to a simplified environment through which they can access all of their data and put it to use in AI systems. It unifies all of the tools found in Amazon's previously disparate ecosystem of developer studios, query editors and visual tools found in platforms such as Amazon Bedrock, Amazon EMR, Amazon Redshift, AWS Glue and the existing SageMaker Studio, the company said. It's all about making everything easier to access, so users can discover and prepare data, create queries and code, and build AI models all in one place. Besides having everything in one place, developers will also benefit from Amazon Q Developer, an AI-powered assistant that can aid in data discovery. They can ask questions such as what data they should be looking at to get a better idea of their organization's product sales, and immediately obtain the answers they need, AWS said. Amazon Bedrock's integrated development environment is also integrated in SageMaker Unified Studio, making it possible to build AI applications using an extensive library of high-performance foundation models, together with various pre-made AI agents, knowledge bases, guardrails and workflows. The company said it's integrating everything in response to the way customers are using SageMaker today. It explained that it has seen how most users also leverage its data analytics tools to support the tasks they're doing with SageMaker, and so it just makes sense to bring them all under one hood to enable easier access. As for the SageMaker Catalog, it's built atop Amazon DataZone and provides access to hundreds of approved AI models together with safeguards such as granular access controls and AI guardrails. They can prevent AI applications from exhibiting toxic or biased behavior. With the arrival of SageMaker Lakehouse, SageMaker users gain the ability to centralize access to the underlying data assets that power their AI models, along with analytics capabilities. One advantage of this setup is that it makes it easier to combine data from multiple sources, such as in Amazon S3 data lakes, Redshift data warehouses or other, federated data sources. SageMaker Lakehouse itself can be accessed via the SageMaker Unified Studio. It also makes it simpler for users to query data, as SageMaker Lakehouse is compatible with the open Apache Iceberg data standard, meaning the information within it can be explored with various SQL analytics tools. The pharmaceutical giant F. Hoffmann-La Roche AG has been using SageMaker Lakehouse in early access, and says it was able to eliminate data silos and make information easier to access, without any complicated data movement procedures. As a result, it's seeing a 40% reduction in data processing times, it said. As it strives to make life easier for developers, AWS is also announcing what it says are "zero-ETL integrations" with various third-party software-as-a-service applications. For instance, customers can integrate Amazon Aurora MySQL, PostgreSQL and other kinds of databases directly with Amazon SageMaker, without needing to build any complex data pipelines first. The zero-ETL integrations eliminate the complex "extract, transact and load" process that's traditionally required to change the format of data found in one application to meet the requirements of another. Essentially, AWS does all of this itself, and it's a big benefit because building data pipelines can be a time-consuming and error-prone process that creates major headaches, even for the biggest organizations. AWS Vice President of Data and AI Swami Sivasubramanian said the convergence of AI and data analytics means that companies are reliant on "increasingly interconnected" data sources, hence the need to make all of that information more accessible. "Many customers are already using combinations of our purpose-built analytics and machine learning tools, such as Amazon SageMaker, Amazon EMR, Amazon Redshift, and more," he said. "The next generation of SageMaker brings together those capabilities, along with some exciting new features, to give customers all the tools they need for data processing, SQL analytics, model development and training, directly within it."

Integrated AI platform: AWS unveils enhancements - SiliconANGLE

Enhancements for SageMaker and Bedrock highlight AWS vision for integrated AI platform Product announcements from Amazon Web Services Inc. this week highlighted the cloud giant's interest in positioning itself at the center of the artificial intelligence conversation by offering a fully integrated AI platform. Key elements in that approach involve the firm's SageMaker and Bedrock offerings. "We want AWS to be the best place for customers to build generative AI applications," said Baskar Sridharan (pictured), vice president of artificial intelligence and machine learning, service and infrastructure, at AWS. "We want SageMaker, we want Bedrock, and we want the data processing capabilities that we launched to be the easiest and most scalable way for you to get to market really quickly and very efficiently. Whether you're trying to process data or whether you're trying to train and build a model, or whether you're running inference, that's the vision." Sridharan spoke with theCUBE Research's John Furrier for theCUBE's "Cloud AWS re:Invent Coverage," during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed key elements of the company's strategy for delivering an integrated AI platform. This week's announcements from AWS regarding SageMaker, the company's neural network development platform, were designed to support structured query language analytics and petabyte-scale data processing. The latest updates were focused on making SageMaker a fully integrated platform for AI development. "We are saying the next generation of SageMaker is the unified experience that will move our customers and help more customers, from data to analytics to modeling development and also to help generative AI app development," Sridharan said. AWS also released major new capabilities for Amazon Bedrock, the company's managed service that makes high-performing foundation models available for use through a unified API. The cloud giant announced the launch of Amazon Nova on Tuesday, a set of six models designed for rapid inference and efficiency that will be integrated into Bedrock. "We now consider Bedrock as the platform for inference workloads on AWS," Sridharan said. "The key element here for us is how we help our customers write and build their generative AI applications while understanding that there's not going to be one model that rules them all." Here's the complete video interview, part of SiliconANGLE's and theCUBE's "Cloud AWS re:Invent Coverage":

AWS SageMaker is transforming into a combined data and AI hub

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Today at its annual huge conference re:Invent 2024, Amazon Web Services (AWS) announced the next generation of its cloud-based machine learning (ML) development platform SageMaker, transforming it a unified hub that allows enterprises to bring together not only all their data assets -- spanning across different data lakes and sources in the lakehouse architecture -- but also a comprehensive set of AWS ecosystem analytics and formerly disparate ML tools. In other words: no longer will Sagemaker just be a place to build AI and machine learning apps -- now you can link your data and derive analytics from it, too. The move comes in response to a general trend of convergence of analytics and AI, where enterprise users have been seen using their data in interconnected ways, right from powering historical analytics to enabling ML model training and generative AI applications targeting different use cases. "Many customers already use combinations of our purpose-built analytics and ML tools (in isolation), 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," Swami Sivasubramanian, the vice president of Data and AI at AWS, said in a statement. SageMaker Unified Studio and Lakehouse at the heart Amazon SageMaker has long been a critical tool for developers and data scientists, providing them with a fully managed service to deploy production-grade ML models. The platform's integrated development environment, SageMaker Studio, gives teams a single, web-based visual interface to perform all machine learning development steps, right from data preparation, model building, training, tuning, and deployment. However, as enterprise needs continue to evolve, AWS realized that keeping SageMaker restricted to just ML deployment doesn't make sense. Enterprises also need purpose-built analytics services (supporting workloads like SQL analytics, search analytics, big data processing, and streaming analytics) in conjunction with existing SageMaker ML capabilities and easy access to all their data to drive insights and power new experiences for their downstream users. Two new capabilities: SageMaker Lakehouse and Unified Studio To bridge this gap, the company has now upgraded SageMaker with two key capabilities: Amazon SageMaker Lakehouse and Unified Studio. The lakehouse offering, as the company explains, provides unified access to all the data stored in the data lakes built on top of Amazon Simple Storage Service (S3), Redshift data warehouses and other federated data sources, breaking silos and making it easily queryable regardless of where the information is originally stored. "Today, more than one million data lakes are built on Amazon Simple Storage Service... allowing customers to centralize their data assets and derive value with AWS analytics, AI, and ML tools... 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," the company noted in a press release. Once all the data is unified with the lakehouse offering, enterprises can access it and put it to work with the other key capability -- SageMaker Unified Studio. At the core, the studio acts as a unified environment that strings together all existing AI and analytics capabilities from Amazon's standalone studios, query editors, and visual tools - spanning Amazon Bedrock, Amazon EMR, Amazon Redshift, AWS Glue and the existing SageMaker Studio. This avoids the time-consuming hassle of using separate tools in isolation and gives users one place to leverage these capabilities to discover and prepare their data, author queries or code, process the data and build ML models. They can even pull up Amazon Q Developer assistant and ask it to handle tasks like data integration, discovery, coding or SQL generation -- in the same environment. So, in a nutshell, users get one place with all their data and all their analytics and ML tools to power downstream applications, ranging from data engineering, SQL analytics and ad-hoc querying to data science, ML and generative AI. Bedrock in Sagemaker For instance, with Bedrock capabilities in the SageMaker Studio, users can connect their preferred high-performing foundation models and tools like Agents, Guardrails and Knowledge Bases with their lakehouse data assets to quickly build and deploy gen AI applications. Once the projects are executed, the lakehouse and studio offerings also allow teams to publish and share their data, models, applications and other artifacts with their team members - while maintaining consistent access policies using a single permission model with granular security controls. This accelerates the discoverability and reuse of resources, preventing duplication of efforts. Compatible with open standards Notably, SageMaker Lakehouse is compatible with Apache Iceberg, meaning it will also work with familiar AI and ML tools and query engines compatible with Apache Iceberg open standard. Plus, it includes zero-ETL integrations for Amazon Aurora MySQL and PostgreSQL, Amazon RDS for MySQL, Amazon DynamoDB with Amazon Redshift as well as SaaS applications like Zendesk and SAP. "SageMaker offerings underscore AWS' strategy of exposing its advanced, comprehensive capabilities in a governed and unified way, so it is quick to build, test and consume ML and AI workloads. AWS pioneered the term Zero-ETL, and it has now become a standard in the industry. It is exciting to see that Zero-ETL has gone beyond databases and into apps. With governance control and support for both structured and unstructured data, data scientists can now easily build ML applications," industry analyst Sanjeev Mohan told VentureBeat. New SageMaker is now available The new SageMaker is available for AWS customers starting today. However, the Unified Studio is still in the preview phase. AWS has not shared a specific timeline but noted that it expects the studio to become generally available soon. Companies like Roche and Natwast Group will be among the first users of the new capabilities, with the latter anticipating Unified Studio will result in a 50% reduction in the time required for its data users to access analytics and AI capabilities. Roche, meanwhile, expects a 40% reduction in data processing time with SageMaker Lakehouse.

AI and analytics converge in new generation Amazon SageMaker

Calling everything SageMaker is confusing - but a new name would have been worse says AWS re:Invent Amazon has introduced a new generation of SageMaker at the re:Invent conference in Las Vegas, bringing together analytics and AI, though with some confusion thanks to the variety of services that bear the SageMaker name. SageMaker Unified Studio, now in preview, covers model development, data, analytics, and building generative AI applications. However the old SageMaker remains, now renamed as SageMaker AI, and that has a studio too, distinct from the new one - and also a classic version that is still available. The difference is that SageMaker AI has a narrower focus, on building and training ML models. That said, SageMaker AI is also considered part of Unified Studio, as is Bedrock, a tool for building generative AI applications. Unified Studio can also be used programmatically, via the DataZone API. Further capabilities for Unified Studio are planned, including access to streaming data such as that from Amazon Kinesis, integration with Amazon Quicksight business intelligence, and with OpenSearch search analytics (Amazon's fork of Elasticsearch and Kibana). According to G2 Krishnamoorthy, VP AWS database services, the core of the next-generation SageMaker is Lakehouse, a service introduced here at re:Invent. "We have built an open interoperable data foundation that is very easy for customers to manage," Krishnamoorthy told us. SageMaker Lakehouse combines data in S3 data lakes and Redshift (AWS data warehouse) so it can be queried with SQL as an Apache Iceberg database using tools including AWS Athena or Apache Spark. Lakehouse also supports connections to DynamoDB, Google BigQuery, MySQL, PostgreSQL and Snowflake. Data can be imported or analyzed in place. Via Lakehouse and Unified Studio, the same data can be used for analytics as well as for machine learning and developing generative AI applications. Brian Ross, AWS head of engineering: analytics builder experience, said at a session attended by The Register: "customers say that their analytics workloads are getting bigger, their machine learning workloads are getting bigger, now their generative AI workloads are getting bigger, and they're also starting to converge." The same data is used for analytics, training models, and building knowledgebases for generative AI. "The big challenge with data is trying to find it. It sits somewhere within the organization but where is it? How do I get access to it?" said Ross. He reckons customers tended to build their own enterprise data platforms to solve this problem, using AWS services and tools, but this was costly whereas the new SageMaker offers "a single end to end experience" that supported all these different uses. SageMaker includes low code / no code tools but it is still aimed at what AWS terms "builders" rather than business users. The latter are directed towards Amazon Q Business apps and Amazon Quicksight dashboards, Krishnamoorthy told us. SageMaker capabilities introduced at re:Invent also include flexible training plans for HyperPod, a service introduced a year ago that manages the infrastructure for training models. Using flexible training plans, the user specifies the accelerated compute resources required and the start and end date limits. HyperPod will then propose a detailed schedule and calculate the cost. It appears that there is high demand for accelerated compute and re:Invent attendees were told that using HyperPod is the best way to secure these resources, by taking account of periods of lower usage. Q Developer, Amazon's AI assistant, is embedded into SageMaker Unified Studio. AWS has also added Q Developer to SageMaker Canvas, a SageMaker AI tool for building ML models, for a chat-based user interface for selecting a model type, uploading data, preparing the data, testing and deploying. Pricing is according to the typical AWS model. There is no charge for using SageMaker Unified Studio itself, but most actions consume other AWS resources which will be charged at their usual rate, though some have a free tier which is shown on the SageMaker pricing page. There is some risk, perhaps, that careless experimentation will run up a large bill. Amazon SageMaker was first introduced seven years ago as a service for data scientists and developers, part of the AWS Management Console. SageMaker offered a simple user interface for selecting training data, selecting a machine learning model, training the model, and deploying it to a cluster of Amazon EC2 instances. Today's SageMaker not only has more features, but its scope is expanded. The naming can be confusing, with the overall SageMaker platform including products that are also well known in their own right. Why is it all called SageMaker? "The world of analytics and AI is coming together. So we thought it's fitting for us to say that, the new expanded SageMaker platform is the product or product suite for all data analytics and AI ... so that's the naming confusion," said Krishnamoorthy. "The alternative would have been to come up with a new name, as Microsoft did with Fabric, and then you have to teach everybody all the components that are in there." ®

Amazon SageMaker gets unified data controls | TechCrunch

It's been close to a decade since Amazon Web Services (AWS), Amazon's cloud computing division, announced SageMaker, its platform to create, train, and deploy AI models. While in previous years AWS has focused on greatly expanding SageMaker's capabilities, this year, streamlining was the goal. At its re:Invent 2024 conference, AWS unveiled SageMaker Unified Studio, a single place to find and work with data from across an organization. SageMaker Unified Studio brings together tools from other AWS services, including the existing SageMaker Studio, to help customers discover, prepare, and process data to build models. "We are seeing a convergence of analytics and AI, with customers using data in increasingly interconnected ways," Swami Sivasubramanian, VP of data and AI at AWS, said in a statement. "The next generation of SageMaker brings together capabilities to give customers all the tools they need for data processing, machine learning model development and training, and generative AI, directly within SageMaker." Using SageMaker Unified Studio, customers can publish and share data, models, apps, and other artifacts with members of their team or broader org. The service exposes data security controls and adjustable permissions, as well as integrations with AWS' Bedrock model development platform. AI is built into SageMaker Unified Studio -- to be specific, Q Developer, Amazon's coding chatbot. In SageMaker Unified Studio, Q Developer can answer questions like "What data should I use to get a better idea of product sales?" or "Generate SQL to calculate total revenue by product category." Explained AWS in a blog post, "Q Developer [can] support development tasks such as data discovery, coding, SQL generation, and data integration" in SageMaker Unified Studio. Beyond SageMaker Unified Studio, AWS launched two small additions to its SageMaker product family: SageMaker Catalog and SageMaker Lakehouse. SageMaker Catalog lets admins define and implement access policies for AI apps, models, tools, and data in SageMaker using a single permission model with granular controls. Meanwhile, SageMaker Lakehouse provides connections from SageMaker and other tools to data stored in AWS data lakes, data warehouses, and enterprise apps. AWS says that SageMaker Lakehouse works with any tools compatible with Apache Iceberg standards -- Apache Iceberg being the open source format for large analytic tables. Admins can apply access controls across data in all the analytics and AI tools SageMaker Lakehouse touches, if they wish. In a somewhat related development, SageMaker should now work better with software-as-a-service applications, thanks to new integrations. SageMaker customers can access data from apps like Zendesk and SAP without having to extract, transform, and load that data first. "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," AWS wrote. "Now, customers can use their preferred analytics and machine learning 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, machine learning, and generative AI."

Amazon SageMaker HyperPod cooks up recipes and flexible training plans to accelerate AI development - SiliconANGLE

Amazon SageMaker HyperPod cooks up recipes and flexible training plans to accelerate AI development Amazon Web Services Inc. isn't stopping at transforming Amazon SageMaker into a unified platform for artificial intelligence development tools. It's equally determined to make it easier for builders to access the underlying infrastructure needed to train their next-generation AI models. Today at AWS re:Invent 2024, the company announced some key updates to Amazon SageMaker HyperPod. The changes are intended to help developers get started in AI model training faster, and save weeks on the time it takes to finish those training jobs through more flexible deployment options. They'll also help developers to reduce the costs associated with model training. In addition, Amazon said it's helping SageMaker customers to discover, deploy and make use of various third-party generative AI and machine learning development tools offered by AWS partners, such as Deepchecks Inc., Fiddler Labs Inc. and Comet ML Inc. AWS unveiled SageMaker HyperPod one year ago, at last year's edition of re:Invent, saying it's an infrastructure offering that provides developers with access to on-demand compute clusters for AI training. With SageMaker HyperPod, users can quickly provision clusters of graphics processing units or other AI accelerators via a combination of point-and-click commands or by using relatively simple scripts. It allows them to get started much more quickly than if they were to manually configure the clusters themselves. The company is trying to position SageMaker HyperPod as the infrastructure platform of choice for AI training. It says developers need a specialized platform because model training is a difficult process that requires tons of expertise in terms of managing the underlying clusters and creating special code to distribute those models across multiple clusters. SageMaker HyperPod eases much of that complexity, and with today's updates the process is getting easier than ever, AWS said. One of the main innovations is the new "recipes" that allow customers to quickly customize popular, publicly available models like Llama and Mistral for specific use cases, based on their internal data. The training recipes are meant to make it easier for users to get started, without being bogged down by things such as defining parameters and benchmarking performance. It's offering more than 30 of the recipes at launch, for models such as Llama 2.2 90B, Llama 3.1 405B and Mistral 8x22B. According to AWS, the recipes can help customers get going much faster, by automatically loading training datasets, applying distributed training techniques and automating other aspects of the process. The company reckons its recipes, available now in the SageMaker GitHub repository, can eliminate weeks of iterative evaluation and testing that would normally be required to get started in training AI. "This is going to be a game changer," Swami Sivasubramanian, vice president of data and machine learning services, said at his re:Invent keynote today. In another update, AWS is also making it easier to plan and manage the underlying compute capacity requirements for AI training jobs. With the new, flexible training plans, customers can simply specify their budget, desired completion date and the maximum number of compute resources required for a job, and SageMaker HyperPod will automatically reserve the capacity, set up the necessary clusters and then deploy everything as necessary. If the user's proposed training requirements do not meet the specified completion date or budget, SageMaker HyperPod will automatically suggest various alternative plans of action, such as extending the date range, adding more compute or conducting the training job in another AWS region. When the customer approves a plan, the infrastructure will be provisioned automatically just before it's required, with the right number of instances needed to get the job done according to the customer's timeline. Once again, it saves developers time, and more importantly it helps reduce the uncertainty that comes when customers need to acquire large clusters of GPUs to complete AI development tasks. AWS said an AI startup called Hippocratic AI is already using its flexible training plans to speed up its training processes, and has noticed a fourfold improvement in the time it takes to get its newest models up to scratch. In a final update to SageMaker HyperPod, AWS is introducing new task governance features that promise to give users more control over task prioritization and resource allocation. The new capabilities make it possible for customers to maximize accelerator utilization for specific model training, fine-tuning and inference jobs, reducing overall development costs by up to 40% in some cases, the company said. With a few simple clicks, users can define their priorities for each task, and set up limits on compute resources. Once these priorities and limits are established, SageMaker HyperPod will quickly allocate the relevant resources, while managing task queues automatically so that the higher priority work is always done first. This enables the most critical training jobs to be prioritized at all times. So if a team has an urgent job that needs doing yesterday, they can automatically free up underutilized resources from other, non-urgent jobs that are underway, pausing them so the critical task can be done as fast as possible. The updates announced today weren't all about HyperPod. In addition, AWS is also making it easier to integrate third-party AI development tools with SageMaker. Previously, such integrations took an awful lot of time, involving lots of different steps around monitoring, compliance, data access, provisioning resources and so on. That's all being automated now for select AI applications. According to AWS, the integrations will make it a trivial experience for users to explore, discover, deploy and use various AI developer applications from the likes of Comet, Deepchecks, Fiddler and other companies directly within SageMaker. One advantage of deploying such apps within SageMaker is that there's no need to move data outside of a secure AWS environment, thanks to the platform's integration with multiple kinds of data stores. "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," said Baskar Sridharan, vice president of AI/ML Services and Infrastructure at AWS.

Better together? Why AWS is unifying data analytics and AI services in SageMaker

Demand for end-to-end platforms, the convergence of data and AI, and the evolution of roles in the generative AI era are all driving the change, say analysts. Data warehousing, business intelligence, data analytics, and AI services are all coming together under one roof at Amazon Web Services. This unification of analytics and AI services is perhaps best exemplified by a new offering inside Amazon SageMaker, Unified Studio, a preview of which AWS CEO Matt Garman unveiled at the company's annual re:Invent conference this week. It combines SQL analytics, data processing, AI development, data streaming, business intelligence, and search analytics. Another offering that AWS announced to support the integration is the SageMaker Data Lakehouse, aimed at helping enterprises unify data across Amazon S3 data lakes and Amazon Redshift data warehouses.

New Amazon SageMaker AI Innovations Reimagine How Customers Build and Scale Generative AI and Machine Learning Models By Investing.com

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 LAS VEGAS--(BUSINESS WIRE)--At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com (NASDAQ:AMZN), Inc. company (NASDAQ: AMZN), today announced four new innovations for Amazon SageMaker AI to help customers get started faster with popular publicly available models, maximize training efficiency, lower costs, and use their preferred tools to accelerate generative artificial intelligence (AI) model development. Amazon SageMaker AI is an end-to-end service used by hundreds of thousands of customers to help build, train, and deploy AI models for any use case with fully managed infrastructure, tools, and workflows. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241204660610/en/ 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 (NASDAQ:INTU), 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 40%. Leading startups such as Writer, Luma AI, and Perplexity, and large enterprises such as Thomson Reuters and Salesforce, are accelerating model development thanks to SageMaker HyperPod. Amazon also used SageMaker HyperPod to train the new Amazon Nova models, reducing their training costs, improving the performance of their training infrastructure, and saving them months of manual work that would have been spent setting up their cluster and managing the end-to-end process. 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: 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. Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 108 Availability Zones within 34 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom (TADAWUL:4280) of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud. Millions of customers"including the fastest-growing startups, largest enterprises, and leading government agencies"trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. About Amazon Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth's Most Customer-Centric Company, Earth's Best Employer, and Earth's Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

AWS makes its SageMaker HyperPod AI platform more efficient for training LLMs | TechCrunch

At last year's AWS re:Invent conference, Amazon's cloud computing unit launched SageMaker HyperPod, a platform for building foundation models. It's no surprise then that at this year's re:Invent, the company is announcing a number of updates to the platform, with a focus on making model training and fine-tuning on HyperPod more efficient and cost-effective for enterprises. HyperPod is now in use by companies like Salesforce, Thompson Reuters and BMW and AI startups like Luma, Perplexity, Stability AI and Hugging Face. It's the needs of these customers that AWS is now addressing with today's updates, Ankur Mehrotra, the GM in charge of HyperPod at AWS, told me. One of the challenges these companies face is that there often simply isn't enough capacity for running their LLM training workloads. "Oftentimes, because of high demand, capacity can be expensive as well as it can be hard to find capacity when you need it, how much you need, and exactly where you need it," Mehrotra said. "Then, what may happen is you may find capacity in specific blocks, which may be split across time and also location. Customers may need to start at one place and then move their workload to another place and all that -- and then also set up and reset their infrastructure to do that again and again." To make this easier, AWS is launching what it calls 'flexible training plans.' With this, HyperPod users can set a timeline and budget. Say they want to complete the training of a model within the next two month and expect to need 30 full days of training with a specific GPU type to achieve that. SageMaker HyperPod can then go out, find the best combination of capacity blocks and create a plan to make this happen. SageMaker handles the infrastructure provisioning and runs the jobs (and pauses them when the capacity is not available). Ideally, Mehrotra noted, this can help these businesses avoid overspending by overprovisioning servers for their training jobs. Many times, though, these businesses aren't training models from scratch. Instead, they are fine-tuning models using their own data on top of open weight models and model architectures like Meta's Llama. For them, the SageMaker team is launching HyperPod Recipes. These are benchmarked and optimized recipes for common architectures like Llama and Mistral that encapsulate the best practices for using these models. Mehrotra stressed that these recipes also figure out the right checkpoint frequency for a given workload to ensure that the progress of the training job is saved regularly. As the number of teams working with generative AI in a company grows, different teams will likely provision their own capacity, which in return means that some of those GPUs will sit idle and eat into a company's overall AI budget. To combat this, AWS is now allowing enterprises to essentially pool those resources and create a central command center for allocating GPU capacity based on a project's priority. The system can then allocate resources automatically as needed (or determined by the internal pecking order, which may not always be the same thing). Another capability this enables is for companies to use most of their allocation for running inference during the day to serve their customers and then allocate more of those resources to training during the night, when there is less demand for inferencing. As it turns out, AWS first built this capability for Amazon itself and the company saw the utilization of its cluster go to over 90% because of this new tool. "Organization really want to innovate, and they have so many ideas. Generative AI is such a new technology. There are so many new ideas. And so they do run into these resource and budget constraints. So it's about doing the work more efficiently and we can really help customers reduce costs -- and this overall helps reduce costs by, we've looked at it, up to 40% for organizations."

New Amazon SageMaker AI Innovations Reimagine How Customers Build and Scale Generative AI and Machine Learning Models

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 At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced four new innovations for Amazon SageMaker AI to help customers get started faster with popular publicly available models, maximize training efficiency, lower costs, and use their preferred tools to accelerate generative artificial intelligence (AI) model development. Amazon SageMaker AI is an end-to-end service used by hundreds of thousands of customers to help build, train, and deploy AI models for any use case with fully managed infrastructure, tools, and workflows. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241204660610/en/ "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 40%. Leading startups such as Writer, Luma AI, and Perplexity, and large enterprises such as Thomson Reuters and Salesforce, are accelerating model development thanks to SageMaker HyperPod. Amazon also used SageMaker HyperPod to train the new Amazon Nova models, reducing their training costs, improving the performance of their training infrastructure, and saving them months of manual work that would have been spent setting up their cluster and managing the end-to-end process. 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: 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. Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 108 Availability Zones within 34 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud. Millions of customers -- including the fastest-growing startups, largest enterprises, and leading government agencies -- trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. About Amazon Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth's Most Customer-Centric Company, Earth's Best Employer, and Earth's Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.