Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://dev-social.scikey.ai)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](http://gitlab.suntrayoa.com) concepts on AWS.<br> |
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<br>In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the models as well.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://www.infiniteebusiness.com) that uses reinforcement finding out to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its support learning (RL) step, which was utilized to improve the [model's actions](https://videofrica.com) beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually boosting both importance and [raovatonline.org](https://raovatonline.org/author/yllhilton18/) clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it's geared up to break down complicated inquiries and reason through them in a detailed way. This guided thinking [procedure](https://gitlab.tncet.com) allows the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, sensible reasoning and data interpretation jobs.<br> |
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion [parameters](https://git.gocasts.ir) in size. The MoE architecture permits activation of 37 billion parameters, making it possible for efficient inference by [routing questions](https://maxmeet.ru) to the most appropriate expert "clusters." This method permits the design to concentrate on various problem domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will [utilize](https://thecodelab.online) an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 [distilled](https://dongochan.id.vn) models bring the reasoning abilities of the main R1 model to more efficient architectures based upon popular open [designs](http://wj008.net10080) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to [simulate](https://bdstarter.com) the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor design.<br> |
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<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](http://steriossimplant.com) design, we [advise releasing](https://repo.serlink.es) this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid [damaging](http://www.mizmiz.de) content, and assess models against crucial security requirements. At the time of writing this blog, for DeepSeek-R1 [releases](https://git.rell.ru) on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://git.bugwc.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are [deploying](http://116.236.50.1038789). To ask for a limit boost, develop a limitation boost demand and reach out to your account team.<br> |
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<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up permissions to use guardrails for content filtering.<br> |
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<br>Implementing guardrails with the ApplyGuardrail API<br> |
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<br>Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous content, and assess designs against essential safety requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model reactions deployed on [Amazon Bedrock](https://gitlab.ujaen.es) Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to [produce](https://haitianpie.net) the guardrail, see the GitHub repo.<br> |
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<br>The basic circulation involves the following actions: First, the system an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After receiving the design's output, another guardrail check is applied. If the [output passes](https://rhabits.io) this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show reasoning using this API.<br> |
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br> |
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<br>Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. |
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At the time of composing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other [Amazon Bedrock](http://47.107.92.41234) tooling. |
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2. Filter for DeepSeek as a [supplier](https://gitlab.reemii.cn) and choose the DeepSeek-R1 design.<br> |
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<br>The model detail page supplies essential details about the model's abilities, prices structure, and implementation guidelines. You can discover detailed usage guidelines, including sample API calls and code bits for combination. The model supports different text generation tasks, consisting of material development, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning capabilities. |
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The page likewise includes implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, choose Deploy.<br> |
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<br>You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated. |
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4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Variety of instances, go into a number of instances (in between 1-100). |
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6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. |
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Optionally, you can configure innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might want to [examine](https://topcareerscaribbean.com) these settings to line up with your organization's security and compliance requirements. |
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7. [Choose Deploy](https://freeworld.global) to start using the design.<br> |
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<br>When the implementation is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. |
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8. Choose Open in playground to access an interactive user interface where you can try out various prompts and adjust design specifications like temperature level and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, content for reasoning.<br> |
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<br>This is an excellent way to explore the design's thinking and text generation abilities before incorporating it into your [applications](https://infinirealm.com). The play area provides instant feedback, [assisting](https://gulfjobwork.com) you comprehend how the model reacts to numerous inputs and letting you fine-tune your triggers for optimal results.<br> |
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<br>You can quickly evaluate the model in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run inference using guardrails with the [deployed](https://git.jamarketingllc.com) DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to perform inference using a [released](https://parissaintgermainfansclub.com) DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends a demand to generate text based on a user timely.<br> |
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<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br> |
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<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical methods: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the technique that finest suits your requirements.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, pick Studio in the [navigation](https://wiki.idealirc.org) pane. |
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2. First-time users will be prompted to develop a domain. |
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3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br> |
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<br>The model browser shows available models, with details like the company name and model capabilities.<br> |
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<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 [model card](https://fromkorea.kr). |
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Each model card shows crucial details, including:<br> |
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<br>- Model name |
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- Provider name |
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- Task classification (for example, Text Generation). |
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Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model<br> |
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<br>5. Choose the design card to view the design details page.<br> |
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<br>The design details page consists of the following details:<br> |
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<br>- The design name and supplier details. |
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[Deploy button](https://miggoo.com.br) to release the design. |
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About and [Notebooks tabs](https://municipalitybank.com) with [detailed](https://equipifieds.com) details<br> |
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<br>The About tab includes important details, such as:<br> |
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<br>- Model description. |
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- License details. |
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- Technical specifications. |
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- Usage guidelines<br> |
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<br>Before you release the design, it's suggested to evaluate the model details and license terms to verify compatibility with your usage case.<br> |
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<br>6. Choose Deploy to continue with [deployment](http://www.mitt-slide.com).<br> |
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<br>7. For Endpoint name, use the immediately produced name or create a customized one. |
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8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, go into the variety of [instances](https://bcstaffing.co) (default: 1). |
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Selecting appropriate instance types and counts is crucial for cost and performance optimization. Monitor your deployment to change these settings as needed.Under [Inference](http://jolgoo.cn3000) type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. |
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10. Review all configurations for precision. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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11. Choose Deploy to deploy the model.<br> |
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<br>The implementation process can take numerous minutes to complete.<br> |
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<br>When deployment is total, your endpoint status will alter to InService. At this point, the model is all set to accept inference requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console [Endpoints](https://humlog.social) page, which will show relevant metrics and status details. When the deployment is complete, you can conjure up the design utilizing a SageMaker runtime client and incorporate it with your applications.<br> |
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br> |
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<br>To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed [AWS authorizations](https://degroeneuitzender.nl) and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.<br> |
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<br>You can run extra requests against the predictor:<br> |
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<br>Implement guardrails and run inference with your SageMaker JumpStart predictor<br> |
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<br>Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can [produce](http://43.142.132.20818930) a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:<br> |
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<br>Tidy up<br> |
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<br>To avoid undesirable charges, finish the steps in this area to tidy up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace implementation<br> |
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<br>If you [deployed](https://robbarnettmedia.com) the model using Amazon Bedrock Marketplace, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. |
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2. In the Managed deployments area, locate the endpoint you wish to delete. |
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3. Select the endpoint, and on the Actions menu, choose Delete. |
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4. Verify the endpoint details to make certain you're deleting the right implementation: 1. Endpoint name. |
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2. Model name. |
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3. Endpoint status<br> |
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<br>Delete the SageMaker JumpStart predictor<br> |
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<br>The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br> |
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<br>Conclusion<br> |
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<br>In this post, we [checked](http://kuzeydogu.ogo.org.tr) out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.<br> |
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<br>About the Authors<br> |
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://sportify.brandnitions.com) business develop [ingenious services](http://gsend.kr) utilizing AWS services and accelerated compute. Currently, he is focused on establishing strategies for fine-tuning and optimizing the reasoning performance of large language models. In his leisure time, Vivek delights in hiking, seeing motion pictures, and trying various cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://194.87.97.82:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://119.23.72.7) [accelerators](https://git.morenonet.com) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://gitea.lelespace.top) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://git.szmicode.com:3000) center. She is passionate about building solutions that assist consumers accelerate their [AI](https://wiki.ragnaworld.net) journey and unlock company value.<br> |
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