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<br>Today, we are thrilled to reveal that DeepSeek R1 distilled Llama and [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:GiuseppeGlenelg) Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://101.132.100.8)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion [specifications](http://114.55.169.153000) to develop, experiment, and properly scale your generative [AI](https://www.lokfuehrer-jobs.de) concepts on AWS.<br> |
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<br>In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions 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://xpressrh.com) that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating feature is its reinforcement learning (RL) action, which was used to refine the model's reactions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's equipped to break down intricate inquiries and reason through them in a detailed way. This assisted reasoning procedure enables the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation model that can be incorporated into various workflows such as agents, rational reasoning and information analysis tasks.<br> |
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient inference by routing questions to the most pertinent specialist "clusters." This method permits the design to concentrate on different issue domains while maintaining total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.<br> |
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<br>You can [release](http://lesstagiaires.com) DeepSeek-R1 model either through [SageMaker JumpStart](https://adventuredirty.com) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and assess models against key safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your [generative](http://8.140.244.22410880) [AI](https://nodlik.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 design, you require access to an ml.p5e [circumstances](http://www.iilii.co.kr). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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. To ask for a limitation boost, produce a limitation boost demand and connect to your account team.<br> |
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<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up approvals 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 enables you to introduce safeguards, avoid harmful content, and assess designs against essential security criteria. You can execute safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br> |
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<br>The basic circulation involves the following actions: First, the system gets an input for the model. This input is then through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for inference. After receiving the model's output, another guardrail check is [applied](http://89.251.156.112). If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in 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 areas 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 gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. |
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At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a [supplier](https://tokemonkey.com) and pick the DeepSeek-R1 design.<br> |
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<br>The design detail page provides necessary details about the model's capabilities, pricing structure, and application standards. You can find detailed use directions, consisting of sample API calls and [code bits](https://git.blinkpay.vn) for combination. The model supports various text generation jobs, including content creation, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT thinking abilities. |
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The page also includes release alternatives and licensing [details](https://diskret-mote-nodeland.jimmyb.nl) to assist you start with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, pick Deploy.<br> |
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<br>You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. |
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4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). |
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5. For Variety of instances, get in a number of circumstances (between 1-100). |
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6. For Instance type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [suggested](http://60.205.104.1793000). |
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Optionally, you can set up advanced security and infrastructure settings, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:Bettina5096) consisting of virtual private cloud (VPC) networking, service function consents, and encryption settings. For most use cases, the default settings will work well. However, for production releases, you might wish to review these settings to align with your company's security and compliance requirements. |
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7. Choose Deploy to begin using the model.<br> |
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<br>When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. |
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8. Choose Open in play area to access an interactive interface where you can try out different prompts and change design specifications like temperature level and optimum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, material for inference.<br> |
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<br>This is an outstanding way to check out the [model's reasoning](https://ai.ceo) and text generation abilities before incorporating it into your applications. The play ground offers instant feedback, assisting you comprehend how the model reacts to various inputs and [letting](https://newvideos.com) you tweak your [triggers](https://crmthebespoke.a1professionals.net) for ideal results.<br> |
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<br>You can quickly test the design in the [play ground](https://git.thatsverys.us) through the UI. However, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:LawerenceIsenber) to conjure up the released model [programmatically](http://charmjoeun.com) with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run reasoning using guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can [produce](http://103.205.66.473000) a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:JanelleJevons) and sends out a demand to produce text based upon a user prompt.<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) center with FMs, integrated algorithms, and prebuilt ML options 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 deploy them into production utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical approaches: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you pick the technique that best matches your requirements.<br> |
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<br>Deploy DeepSeek-R1 through [SageMaker JumpStart](https://almagigster.com) UI<br> |
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<br>Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, choose Studio in the navigation 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 [internet browser](https://heyjinni.com) displays available models, with [details](https://rocksoff.org) like the supplier name and design capabilities.<br> |
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. |
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Each design card reveals essential details, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task category (for instance, [garagesale.es](https://www.garagesale.es/author/toshahammon/) Text Generation). |
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Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design<br> |
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<br>5. Choose the [design card](http://39.108.83.1543000) to see the model details page.<br> |
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<br>The design details page includes the following details:<br> |
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<br>- The design name and [supplier details](https://www.yanyikele.com). |
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Deploy button to release the model. |
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About and Notebooks tabs with detailed details<br> |
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<br>The About tab consists of [crucial](https://www.mapsisa.org) 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 deploy the model, it's recommended to review the design details and license terms to verify compatibility with your use case.<br> |
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<br>6. Choose Deploy to continue with release.<br> |
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<br>7. For Endpoint name, use the automatically created name or [develop](https://lubuzz.com) a custom one. |
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8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, go into the variety of instances (default: 1). |
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Selecting suitable instance types and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Homer93G479471) counts is crucial for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. |
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10. Review all configurations for precision. For this model, we strongly [recommend sticking](https://mediawiki1334.00web.net) to SageMaker JumpStart default settings and making certain that network isolation remains in location. |
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11. Choose Deploy to deploy the design.<br> |
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<br>The implementation procedure can take a number of minutes to finish.<br> |
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<br>When deployment is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept reasoning requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.<br> |
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<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br> |
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<br>To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for [deploying](https://merimnagloballimited.com) the design is [supplied](https://git.alexhill.org) in the Github here. You can clone the notebook and range from SageMaker Studio.<br> |
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<br>You can run additional demands against the predictor:<br> |
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<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br> |
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<br>Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as revealed in the following code:<br> |
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<br>Tidy up<br> |
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<br>To avoid unwanted charges, finish the steps in this section to tidy up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace deployment<br> |
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<br>If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. |
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2. In the Managed releases section, find the endpoint you wish to delete. |
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3. Select the endpoint, and on the Actions menu, pick Delete. |
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4. Verify the endpoint details to make certain you're erasing the correct release: 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 released will sustain costs if you leave it [running](https://www.keeloke.com). Use the following code to delete the endpoint if you want 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 [explored](https://www.yaweragha.com) how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and [SageMaker JumpStart](https://www.punajuaj.com). 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 designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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](http://121.28.134.382039) at AWS. He helps emerging generative [AI](https://farmwoo.com) companies build ingenious options using AWS services and sped up compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning efficiency of large language designs. In his complimentary time, Vivek delights in treking, watching films, and trying various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://merimnagloballimited.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://code.estradiol.cloud) accelerators (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://nextodate.com) with the Third-Party Model [Science](https://gitlab.xfce.org) group at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [surgiteams.com](https://surgiteams.com/index.php/User:KelleeKinsey) generative [AI](https://getquikjob.com) hub. She is passionate about constructing options that help clients accelerate their [AI](http://117.72.17.132:3000) journey and unlock service worth.<br> |
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