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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://www.seekbetter.careers)'s first-generation [frontier](http://git.hiweixiu.com3000) model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:Theda61T23387) and responsibly scale your generative [AI](https://git.newpattern.net) concepts on AWS.<br> |
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<br>In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to [release](https://collegetalks.site) the distilled versions of the designs 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) established by DeepSeek [AI](http://106.15.120.127:3000) that uses [support discovering](https://git.rankenste.in) to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating feature is its support knowing (RL) action, which was used to improve the design's reactions beyond the basic pre-training and [tweak procedure](https://eliteyachtsclub.com). By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's equipped to break down complicated questions and reason through them in a detailed manner. This directed reasoning procedure permits the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be integrated into various workflows such as representatives, logical reasoning and data interpretation jobs.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, allowing effective reasoning by routing inquiries to the most relevant specialist "clusters." This [approach](https://my-estro.it) enables the model to concentrate on different issue domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more [effective models](https://playtube.app) to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.<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 design, we advise releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate designs against key security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on [SageMaker JumpStart](http://43.142.132.20818930) and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can [develop](https://code.thintz.com) multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://candidates.giftabled.org) 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 circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:BerndRicardo) 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 releasing. To ask for a limitation boost, produce a limit increase request 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 use Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for content filtering.<br> |
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<br>Implementing guardrails with the [ApplyGuardrail](https://losangelesgalaxyfansclub.com) API<br> |
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<br>[Amazon Bedrock](https://myjobasia.com) Guardrails allows you to introduce safeguards, avoid damaging material, and evaluate designs against essential security requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop 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 flow includes the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) it's sent out to the design for reasoning. After getting the model's output, another guardrail check is used. 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 showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections demonstrate [inference](https://lastpiece.co.kr) utilizing 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, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. |
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At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.<br> |
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<br>The design detail page provides vital details about the design's abilities, pricing structure, and execution standards. You can find detailed usage guidelines, [including sample](http://110.42.231.1713000) API calls and code snippets for [combination](https://foris.gr). The design supports various text generation tasks, including content development, code generation, and concern answering, using its support discovering optimization and CoT thinking capabilities. |
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The page also consists of release alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, select Deploy.<br> |
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<br>You will be prompted to configure the implementation 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, enter a variety of circumstances (in between 1-100). |
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6. For example type, [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:MuhammadRosenber) choose your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. |
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Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1103710) encryption settings. For most use cases, the default settings will work well. However, for [photorum.eclat-mauve.fr](http://photorum.eclat-mauve.fr/profile.php?id=255848) production implementations, you might want to evaluate these settings to line up with your company's security and compliance requirements. |
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7. Choose Deploy to begin using the design.<br> |
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<br>When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. |
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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 using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For instance, material for reasoning.<br> |
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<br>This is an outstanding way to check out the model's thinking and text generation capabilities before integrating it into your applications. The play area provides immediate feedback, helping you [comprehend](http://gitea.shundaonetwork.com) how the model reacts to various inputs and letting you fine-tune your prompts for [ideal outcomes](http://president-park.co.kr).<br> |
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<br>You can quickly check the design in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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 created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends a request to [produce text](http://103.197.204.1633025) based on 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, built-in algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you select the method that finest suits your [requirements](https://namoshkar.com).<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 DeepSeek-R1 using 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 triggered to create 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 displays available models, with [details](https://git.limework.net) like the 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, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task category (for instance, Text Generation). |
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Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design<br> |
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<br>5. Choose the model card to view 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 company details. |
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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 includes important details, such as:<br> |
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<br>- Model description. |
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- License details. |
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[- Technical](https://reklama-a5.by) specs. |
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- Usage guidelines<br> |
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<br>Before you deploy the model, it's recommended to review 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 release.<br> |
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<br>7. For Endpoint name, use the immediately produced name or develop a custom-made 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 number of circumstances (default: 1). |
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Selecting appropriate instance types and counts is crucial for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. |
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10. Review all setups for precision. For this design, we highly advise [sticking](http://kpt.kptyun.cn3000) to SageMaker JumpStart default settings and making certain that network isolation remains in place. |
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11. Choose Deploy to deploy the model.<br> |
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<br>The release process can take a number of minutes to finish.<br> |
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<br>When implementation is total, your [endpoint status](https://git.cooqie.ch) will change to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and [status details](https://socials.chiragnahata.is-a.dev). When the implementation is total, you can [conjure](https://chancefinders.com) up the model using 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 get begun 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 and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for [releasing](http://47.99.132.1643000) the model is in the Github here. You can clone the note pad 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 inference with your SageMaker JumpStart predictor<br> |
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<br>Similar to Amazon Bedrock, you can likewise utilize the [ApplyGuardrail API](https://git.karma-riuk.com) with your SageMaker JumpStart predictor. You can create a [guardrail utilizing](http://gitlab.together.social) 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 prevent undesirable charges, finish the actions in this section 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 design utilizing Amazon Bedrock Marketplace, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. |
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2. In the [Managed implementations](http://gitlab.qu-in.com) area, find the [endpoint](https://jobistan.af) you desire to erase. |
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3. Select the endpoint, and on the [Actions](http://bh-prince2.sakura.ne.jp) menu, select Delete. |
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4. Verify the endpoint details to make certain you're erasing the proper 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 model you released will sustain costs if you leave it running. Use the following code to erase the endpoint if you desire 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 release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://rabota.newrba.ru) or Amazon [Bedrock Marketplace](https://busanmkt.com) now to start. For more details, refer to Use [Amazon Bedrock](http://124.220.187.1423000) tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon [Bedrock](https://syndromez.ai) Marketplace, and Getting started 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](http://wp10476777.server-he.de) companies build ingenious options using AWS services and sped up calculate. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning efficiency of large language models. In his complimentary time, Vivek delights in hiking, viewing films, and attempting different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://funnyutube.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://gitea.dokm.xyz) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br> |
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<br>Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://git.tea-assets.com) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.brass.host) hub. She is passionate about constructing services that help consumers accelerate their [AI](https://www.cupidhive.com) journey and unlock service value.<br> |
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