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<br>Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://git.bourseeye.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://janhelp.co.in) ideas on AWS.<br>
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<br>In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the designs too.<br>
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<br>Overview of DeepSeek-R1<br>
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<br>DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://siman.co.il) that uses reinforcement learning to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying feature is its reinforcement knowing (RL) step, which was utilized to improve the design's actions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's geared up to break down complicated questions and reason through them in a detailed manner. This [guided thinking](http://8.137.12.293000) [procedure permits](https://www.assistantcareer.com) the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be integrated into different workflows such as agents, sensible thinking and data analysis tasks.<br>
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective inference by routing queries to the most appropriate professional "clusters." This technique allows the model to concentrate on different issue domains while maintaining general efficiency. 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 [release](https://www.facetwig.com) the design. ml.p5e.48 [xlarge features](http://git.taokeapp.net3000) 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the thinking of the main R1 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). [Distillation refers](http://101.42.90.1213000) to a process of training smaller, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:JaneenLumpkins6) more efficient designs to mimic the behavior and thinking patterns of the larger DeepSeek-R1 design, using 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 design, we suggest deploying this model with guardrails in location. In this blog, we will [utilize Amazon](https://woodsrunners.com) Bedrock Guardrails to [introduce](http://120.79.75.2023000) safeguards, prevent hazardous material, and evaluate designs against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://148.66.10.10:3000) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 circumstances in the AWS Region you are deploying. To [request](https://git.lain.church) a limitation boost, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) produce a limitation boost request and connect to your account group.<br>
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<br>Because you will be deploying this design 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 directions, see Establish permissions to utilize guardrails for material filtering.<br>
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<br>Implementing guardrails with the ApplyGuardrail API<br>
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<br>Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging material, and evaluate models against crucial [security criteria](https://www.execafrica.com). You can execute security procedures for the DeepSeek-R1 [design utilizing](https://www.seekbetter.careers) the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design responses deployed 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 create the guardrail, see the GitHub repo.<br>
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<br>The basic flow includes the following actions: First, the system gets an input for [wavedream.wiki](https://wavedream.wiki/index.php/User:AdriannaBranch) the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections demonstrate inference 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 structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<br>
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<br>1. On the Amazon Bedrock console, pick Model catalog 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 conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a [company](https://my.buzztv.co.za) and select the DeepSeek-R1 design.<br>
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<br>The model detail page supplies vital details about the model's capabilities, rates structure, and implementation guidelines. You can find detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports numerous text generation jobs, consisting of content production, code generation, and question answering, using its [reinforcement learning](https://www.infiniteebusiness.com) optimization and CoT thinking capabilities.
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The page also consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications.
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3. To begin using DeepSeek-R1, select Deploy.<br>
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<br>You will be triggered to configure the release details for DeepSeek-R1. The model ID will be [pre-populated](https://www.lokfuehrer-jobs.de).
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4. For [Endpoint](http://www.mizmiz.de) name, enter an endpoint name (in between 1-50 alphanumeric characters).
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5. For Number of instances, go into a variety of circumstances (between 1-100).
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6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
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Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might wish to examine these settings to align with your company's security and compliance requirements.
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7. Choose Deploy to start utilizing the design.<br>
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<br>When the deployment is total, 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 different prompts and adjust model 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 design template for optimum outcomes. For example, content for reasoning.<br>
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<br>This is an exceptional way to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, assisting you comprehend how the design reacts to different inputs and letting you tweak your triggers for optimal results.<br>
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<br>You can rapidly test the design in the playground through the UI. However, to conjure up the deployed model 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](http://git.hongtusihai.com) DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to carry out reasoning using a [released](https://www.diltexbrands.com) DeepSeek-R1 model through Amazon Bedrock [utilizing](https://gitlab.interjinn.com) the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, [pediascape.science](https://pediascape.science/wiki/User:LuannHeane043) utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up [reasoning](http://49.232.207.1133000) criteria, and sends out a request to generate 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 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 data, and deploy them into production using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker [Python SDK](https://knightcomputers.biz). Let's check out both methods to help you pick the method that best matches your requirements.<br>
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<br>Deploy DeepSeek-R1 through [SageMaker JumpStart](https://testing-sru-git.t2t-support.com) UI<br>
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<br>Complete the following [actions](https://writerunblocks.com) to deploy DeepSeek-R1 using SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, select Studio in the navigation pane.
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2. First-time users will be triggered to produce a domain.
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3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
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<br>The model internet [browser](http://1.94.127.2103000) displays available designs, with details like the company name and model abilities.<br>
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
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Each model card shows key details, including:<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 relevant), indicating that this model can be signed up with Amazon Bedrock, [wavedream.wiki](https://wavedream.wiki/index.php/User:ClaireSparling1) enabling you to use Amazon Bedrock APIs to conjure up the model<br>
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<br>5. Choose the model card to see the model details page.<br>
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<br>The model details page includes the following details:<br>
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<br>- The model name and company details.
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Deploy button to release the design.
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About and Notebooks tabs with detailed details<br>
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<br>The About tab includes crucial details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical specs.
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- Usage standards<br>
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<br>Before you deploy the model, 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 [proceed](http://git.jaxc.cn) with release.<br>
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<br>7. For Endpoint name, utilize the instantly produced name or develop a customized one.
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8. For [Instance type](http://pakgovtjob.site) ¸ select an instance type (default: ml.p5e.48 xlarge).
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9. For [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:ChassidyLeigh85) Initial circumstances count, go into the variety of circumstances (default: 1).
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Selecting suitable instance types and counts is crucial for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for [sustained traffic](http://101.34.66.2443000) and low latency.
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10. Review all configurations for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
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11. Choose Deploy to deploy the model.<br>
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<br>The deployment procedure can take numerous minutes to finish.<br>
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<br>When release is total, your endpoint status will change to InService. At this point, the design is prepared to accept inference requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your [applications](https://redebrasil.app).<br>
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<br>Deploy DeepSeek-R1 using the [SageMaker Python](https://gitlab.isc.org) SDK<br>
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<br>To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the [SageMaker Python](http://1.12.246.183000) SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.<br>
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<br>You can run additional requests 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 [develop](https://www.virtuosorecruitment.com) a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:<br>
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<br>Clean up<br>
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<br>To avoid undesirable charges, complete the steps in this area to tidy up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace release<br>
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<br>If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following steps:<br>
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments.
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2. In the [Managed deployments](https://gitlab.kitware.com) section, locate the [endpoint](https://git.jerl.dev) you want to erase.
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3. Select the endpoint, and on the [Actions](https://samisg.eu8443) menu, select Delete.
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4. Verify the endpoint details to make certain you're deleting the correct implementation: 1. Endpoint name.
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2. Model name.
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3. [Endpoint](https://funnyutube.com) status<br>
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<br>Delete the SageMaker JumpStart predictor<br>
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<br>The SageMaker JumpStart model you deployed will sustain costs if you leave it [running](https://cvwala.com). 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 out how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](http://www.buy-aeds.com) now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Josef06S8821379) Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker [JumpStart](https://frce.de).<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://stepaheadsupport.co.uk) companies develop ingenious solutions using AWS services and sped up calculate. Currently, he is focused on developing strategies for fine-tuning and [optimizing](http://161.97.176.30) the inference efficiency of large language designs. In his free time, Vivek enjoys treking, viewing motion pictures, and trying various foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://www.pkjobs.store) Specialist Solutions Architect with the Third-Party Model [Science](https://connectworld.app) team at AWS. His location of focus is AWS [AI](https://git.molokoin.ru) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
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<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://116.62.118.242) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://git.bzgames.cn) center. She is enthusiastic about developing options that help customers accelerate their [AI](http://8.141.155.183:3000) journey and unlock organization worth.<br>
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