Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md
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DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md
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<br>Today, we are excited 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://cloudsound.ideiasinternet.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](https://82.65.204.63) 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 comparable actions to release the distilled variations of the models also.<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](http://test.9e-chain.com) that uses support learning to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating feature is its reinforcement knowing (RL) step, which was utilized to fine-tune the model's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's equipped to break down intricate inquiries and factor through them in a [detailed manner](https://www.xtrareal.tv). This guided reasoning procedure enables the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation design that can be [integrated](http://93.104.210.1003000) into different workflows such as agents, sensible reasoning and information interpretation jobs.<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 allows activation of 37 billion specifications, allowing effective [inference](https://gitlab.thesunflowerlab.com) by routing queries to the most appropriate specialist "clusters." This method enables the design to focus on various problem domains while maintaining general effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and [it-viking.ch](http://it-viking.ch/index.php/User:TamLivingston31) Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient models to mimic the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing it as a teacher 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 advise releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and examine models against essential security criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://techport.io) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 design, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ArronRunyon8868) you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're using 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 releasing. To ask for a limitation increase, produce a limit increase request and connect to your account team.<br>
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to [utilize Amazon](https://apkjobs.com) Bedrock Guardrails. For guidelines, see Set up approvals to use 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 enables you to present safeguards, prevent damaging content, and evaluate designs against crucial safety criteria. You can carry out security steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br>
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<br>The basic circulation involves the following actions: 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, it's sent out to the model for inference. After receiving 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, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1321201) a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The [examples](https://dalilak.live) showcased in the following areas 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 provides 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 actions:<br>
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<br>1. On the Amazon Bedrock console, pick Model brochure 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 doesn't support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.<br>
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<br>The design detail page provides vital details about the design's capabilities, rates structure, and [implementation standards](http://git.nextopen.cn). You can find [detailed](https://careerconnect.mmu.edu.my) usage directions, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:ChiquitaVeilleux) consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, including material development, code generation, and question answering, utilizing its reinforcement learning optimization and CoT reasoning capabilities.
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The page likewise includes implementation options and licensing details to help you get going with DeepSeek-R1 in your applications.
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3. To begin utilizing DeepSeek-R1, [select Deploy](http://tv.houseslands.com).<br>
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<br>You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073855) go into an endpoint name (between 1-50 alphanumeric characters).
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5. For Variety of instances, go into a number of instances (between 1-100).
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6. For Instance type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
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Optionally, you can configure sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and [file encryption](https://shankhent.com) settings. For most use cases, the default settings will work well. However, for production deployments, you may wish to [examine](http://8.217.113.413000) these settings to line up 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 complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
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8. Choose Open in playground to access an interactive user interface where you can try out various prompts and adjust model parameters like temperature 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, content for inference.<br>
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<br>This is an excellent way to explore the model's thinking and text generation abilities before incorporating it into your applications. The [playground](https://ozoms.com) provides instant feedback, helping you [comprehend](https://www.remotejobz.de) how the [model reacts](http://222.85.191.975000) to different inputs and letting you tweak your triggers for ideal results.<br>
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<br>You can quickly [evaluate](https://www.ayurjobs.net) 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 inference using guardrails with the deployed DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. 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 criteria, and sends a demand to create 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, integrated algorithms, and prebuilt ML solutions that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the [SageMaker Python](https://deadlocked.wiki) SDK. Let's check out both to help you select the method that finest suits your requirements.<br>
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
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<br>Complete the following actions to deploy DeepSeek-R1 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 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 shows available models, with details like the service provider name and design abilities.<br>
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<br>4. Look for DeepSeek-R1 to view 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 classification (for example, Text Generation).
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Bedrock Ready badge (if applicable), showing that this model can be registered with Amazon Bedrock, allowing you to [utilize Amazon](https://nextcode.store) Bedrock APIs to invoke the design<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 [consists](http://39.108.86.523000) of the following details:<br>
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<br>- The design name and company 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 essential details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical requirements.
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- Usage guidelines<br>
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<br>Before you deploy the design, it's suggested to review the model details and license terms to validate compatibility with your use case.<br>
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<br>6. Choose Deploy to proceed with [implementation](https://comunidadebrasilbr.com).<br>
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<br>7. For Endpoint name, utilize the automatically produced name or develop a customized one.
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8. For Instance type ¸ pick an [instance type](http://39.98.253.1923000) (default: ml.p5e.48 xlarge).
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9. For Initial instance count, go into the number of instances (default: 1).
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Selecting appropriate instance types and counts is essential for cost and performance optimization. Monitor your release to adjust these [settings](https://holisticrecruiters.uk) as needed.Under [Inference](http://47.100.17.114) type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
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10. Review all setups for precision. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
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11. Choose Deploy to release the model.<br>
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<br>The implementation procedure can take numerous minutes to complete.<br>
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<br>When implementation is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime client 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals 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 [deploying](https://git.protokolla.fi) the design is [offered](https://ashawo.club) in the Github here. You can clone the notebook 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 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 produce a guardrail using the [Amazon Bedrock](https://napvibe.com) console or the API, and execute it as displayed 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 deployment<br>
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<br>If you deployed the design using 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, choose Marketplace deployments.
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2. In the Managed implementations area, locate the endpoint you wish to delete.
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3. Select the endpoint, and on the [Actions](https://www.joinyfy.com) menu, choose 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 model you [released](http://gitlab.xma1.de) will sustain costs if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete [Endpoints](https://circassianweb.com) 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 release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.<br>
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<br>About the Authors<br>
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<br>[Vivek Gangasani](https://git.cloud.krotovic.com) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://git.novisync.com) business construct innovative services utilizing AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and enhancing the inference performance of big language designs. In his spare time, Vivek delights in hiking, watching motion pictures, and trying various foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://www.hyakuyichi.com:3000) Specialist Solutions [Architect](https://optimaplacement.com) with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://47.106.228.113:3000) [accelerators](http://123.60.103.973000) (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 working on generative [AI](https://jobsekerz.com) with the Third-Party Model Science team at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon [SageMaker](https://git.guaranteedstruggle.host) JumpStart, [SageMaker's artificial](https://jobdd.de) intelligence and generative [AI](https://ipmanage.sumedangkab.go.id) center. She is passionate about developing options that assist customers accelerate their [AI](https://wik.co.kr) journey and unlock business worth.<br>
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