Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are excited to reveal 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://octomo.co.uk)'s first-generation [frontier](https://git.j4nis05.ch) design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](https://blablasell.com) ideas on AWS.<br> |
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<br>In this post, [wavedream.wiki](https://wavedream.wiki/index.php/User:TammieRaposo6) we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps 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 design (LLM) developed by DeepSeek [AI](http://profilsjob.com) that uses reinforcement finding out to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement knowing (RL) step, which was utilized to refine the design's actions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated queries and factor through them in a detailed manner. This directed thinking procedure allows the model to produce more accurate, transparent, and [detailed responses](https://gitlab.chabokan.net). This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be integrated into various workflows such as agents, logical thinking and information interpretation tasks.<br> |
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, [allowing effective](http://120.77.205.309998) inference by routing questions to the most relevant specialist "clusters." This approach permits the model to concentrate on various problem domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for [reasoning](http://profilsjob.com). In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the [thinking abilities](https://e-gitlab.isyscore.com) of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient designs to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.<br> |
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<br>You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CharleyRudall29) we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine models against key security criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and [Bedrock](https://employmentabroad.com) Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can [develop](http://copyvance.com) several [guardrails tailored](https://src.enesda.com) to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://code.karsttech.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To 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 circumstances in the AWS Region you are deploying. To ask for a limit boost, develop a limit boost request and connect to your account group.<br> |
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<br>Because you will be releasing this model with [Amazon Bedrock](https://git.qoto.org) Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize 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 present safeguards, avoid damaging content, and examine designs against essential security requirements. You can execute safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model actions released on [Amazon Bedrock](http://leovip125.ddns.net8418) Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br> |
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<br>The general circulation involves the following actions: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](http://football.aobtravel.se). If the input passes the guardrail check, it's sent out to the model for inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the final outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or [output phase](https://jobsubscribe.com). The examples showcased in the following sections demonstrate [reasoning](https://careerjunction.org.in) 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 offers you access to over 100 popular, emerging, and specialized structure designs (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, choose Model brochure under Foundation models in the [navigation](http://revoltsoft.ru3000) pane. |
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At the time of composing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.<br> |
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<br>The model detail page provides [vital details](http://git2.guwu121.com) about the model's capabilities, rates structure, and [application guidelines](https://probando.tutvfree.com). You can discover detailed use guidelines, consisting of sample API calls and code snippets for integration. The model supports various text generation tasks, [including](https://akrs.ae) content development, code generation, and question answering, utilizing its [reinforcement discovering](https://krotovic.cz) optimization and CoT reasoning capabilities. |
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The page also consists of implementation options and [garagesale.es](https://www.garagesale.es/author/seanedouard/) licensing details to assist you get started with DeepSeek-R1 in your applications. |
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3. To begin utilizing DeepSeek-R1, select Deploy.<br> |
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<br>You will be prompted to configure the deployment details for DeepSeek-R1. The model 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 circumstances, enter a variety of instances (in between 1-100). |
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6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. |
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Optionally, you can set up sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you may wish to review these settings to line up with your company's security and compliance requirements. |
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7. Choose Deploy to begin [utilizing](http://116.205.229.1963000) the design.<br> |
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<br>When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play area to access an interactive user interface where you can explore various prompts and change design criteria like temperature level and optimum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, content for reasoning.<br> |
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<br>This is an excellent way to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design responds to various inputs and letting you fine-tune your triggers for ideal results.<br> |
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<br>You can [rapidly test](https://ka4nem.ru) the model in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run inference utilizing guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends a demand to generate text based upon 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) center with FMs, built-in algorithms, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Lawerence56N) and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical techniques: utilizing the instinctive SageMaker [JumpStart](https://www.meetgr.com) UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the technique that finest fits your requirements.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following steps to [release](http://101.34.228.453000) DeepSeek-R1 utilizing SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, pick Studio in the navigation pane. |
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2. First-time users will be prompted to create a domain. |
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3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br> |
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<br>The model internet browser displays available models, with details like the service provider name and model capabilities.<br> |
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. |
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Each design card shows essential 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](https://degroeneuitzender.nl) badge (if relevant), showing that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model<br> |
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<br>5. Choose the design card to see the design details page.<br> |
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<br>The model details page includes the following details:<br> |
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<br>- The design name and supplier details. |
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Deploy button to deploy 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 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 standards<br> |
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<br>Before you release the model, it's recommended to examine the design details and license terms to confirm compatibility with your use case.<br> |
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<br>6. Choose Deploy to proceed with release.<br> |
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<br>7. For Endpoint name, use the automatically created name or produce a customized one. |
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8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, go into the variety of instances (default: 1). |
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Selecting proper circumstances types and counts is essential for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. |
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10. Review all configurations for accuracy. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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11. Choose Deploy to [release](https://wiki.ragnaworld.net) the model.<br> |
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<br>The release process can take several minutes to finish.<br> |
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<br>When deployment is total, your endpoint status will alter to InService. At this moment, the model is all set to accept inference [requests](https://gitea.johannes-hegele.de) through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the [release](http://macrocc.com3000) is total, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1324005) 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 using the SageMaker Python SDK<br> |
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<br>To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS approvals 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 releasing the model is supplied in the Github here. You can clone the note pad and range 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 a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br> |
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<br>Clean up<br> |
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<br>To prevent undesirable charges, complete 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 released the model utilizing Amazon Bedrock Marketplace, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. |
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2. In the [Managed releases](http://carvis.kr) area, locate the endpoint you desire to delete. |
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3. Select the endpoint, and on the Actions menu, select Delete. |
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4. Verify the endpoint details to make certain you're deleting the correct deployment: 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](https://www.womplaz.com). Use the following code to erase 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 explored how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://git.maxwellj.xyz) now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart [pretrained](https://thathwamasijobs.com) models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:DavidShackelford) 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 helps emerging generative [AI](https://aquarium.zone) companies develop innovative services utilizing AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the [inference efficiency](http://git.edazone.cn) of large language designs. In his spare time, Vivek takes pleasure in treking, enjoying films, and attempting various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://gitlab.chabokan.net) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://encone.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br> |
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<br>Jonathan Evans is a [Professional Solutions](https://tube.denthubs.com) Architect dealing with generative [AI](http://gitlab.sybiji.com) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://selfyclub.com) center. She is passionate about building solutions that help consumers accelerate their [AI](https://turizm.md) journey and unlock service worth.<br> |
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