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<br>Today, we are delighted to reveal that DeepSeek R1 distilled Llama and Qwen models are available through [Amazon Bedrock](https://gitea.ci.apside-top.fr) Marketplace and [Amazon SageMaker](https://suomalaistajalkapalloa.com) JumpStart. With this launch, you can now release DeepSeek [AI](http://forum.rcsubmarine.ru)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](http://plus.ngo) 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 steps to release the distilled variations of the designs too.<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](https://www.lshserver.com:3000) that utilizes reinforcement finding out to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its [support](https://ou812chat.com) [learning](https://git.bwnetwork.us) (RL) action, which was used to fine-tune the model's responses beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually improving both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, [indicating](https://pioneercampus.ac.in) it's geared up to break down intricate questions and reason through them in a detailed manner. This assisted thinking procedure allows the design to produce more accurate, transparent, and detailed responses. This [model combines](http://codaip.co.kr) [RL-based fine-tuning](https://hub.bdsg.academy) with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, rational thinking and data analysis jobs.<br>
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, enabling efficient [inference](https://smartcampus-seskoal.id) by [routing inquiries](https://nuswar.com) to the most pertinent specialist "clusters." This technique enables the design to [specialize](https://finitipartners.com) in various problem domains while maintaining general efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 [distilled](http://128.199.125.933000) models bring the thinking capabilities of the main R1 design to more [effective architectures](https://digital-field.cn50443) based upon [popular](http://111.231.76.912095) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.<br>
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<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and examine models against key safety criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your [generative](https://git.wun.im) [AI](http://39.101.160.11:8099) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas [console](http://209.141.61.263000) and under AWS Services, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To ask for a limit increase, create a limit increase request and reach out to your account group.<br>
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<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and [Gain Access](http://47.103.108.263000) To Management (IAM) permissions to use [Amazon Bedrock](http://www.thegrainfather.com.au) Guardrails. For guidelines, 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 enables you to present safeguards, prevent harmful content, and assess models against crucial security criteria. You can carry out precaution for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:CHOEnid1821) the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br>
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<br>The general flow includes the following steps: First, the system receives an input for the design. This input is then [processed](https://rejobbing.com) through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design 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](https://gitlab.surrey.ac.uk) the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate reasoning using this API.<br>
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
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<br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (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, select Model brochure under Foundation designs in the [navigation pane](https://aloshigoto.jp).
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At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.<br>
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<br>The design detail page offers essential details about the model's capabilities, prices structure, and implementation standards. You can find detailed use directions, including sample API calls and code bits for combination. The model supports numerous text generation tasks, consisting of material creation, code generation, and concern answering, utilizing its [support learning](https://git.li-yo.ts.net) optimization and CoT reasoning abilities.
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The page also consists of implementation options and licensing details to help you get started with DeepSeek-R1 in your applications.
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3. To begin utilizing DeepSeek-R1, pick Deploy.<br>
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<br>You will be [triggered](http://xiaomu-student.xuetangx.com) to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
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5. For Number of circumstances, get in a number of circumstances (in between 1-100).
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6. For [Instance](https://git.esc-plus.com) type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
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Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function approvals, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you might want to review these settings to line up with your company's security and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:KathrinSabella) compliance requirements.
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7. Choose Deploy to begin utilizing the model.<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 ground to access an interactive user interface where you can experiment with various triggers and change design criteria like temperature level and maximum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.<br>
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<br>This is an excellent way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The playground supplies immediate feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your triggers for ideal outcomes.<br>
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<br>You can rapidly check the model in the play area 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 deployed DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock [console](https://collegetalks.site) or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1323555) 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, integrated algorithms, and prebuilt ML services that you can with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 model through SageMaker JumpStart offers 2 practical approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you select the method that best matches 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 using SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, choose Studio in the navigation pane.
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2. First-time users will be prompted to produce 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 design browser shows available models, with details like the service provider name and design capabilities.<br>
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
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Each model 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 classification (for instance, Text Generation).
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Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model<br>
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<br>5. Choose the model card to see the design details page.<br>
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<br>The model details page consists of the following details:<br>
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<br>- The design name and service provider 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 consists](https://138.197.71.160) of important 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 guidelines<br>
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<br>Before you deploy the model, it's recommended to review the model details and license terms to [confirm compatibility](https://www.kritterklub.com) with your use case.<br>
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<br>6. Choose Deploy to continue with deployment.<br>
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<br>7. For Endpoint name, utilize the immediately generated name or create a customized one.
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8. For example type ¸ select a circumstances type (default: [pediascape.science](https://pediascape.science/wiki/User:EdytheIvory959) ml.p5e.48 xlarge).
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9. For Initial instance count, get in the variety of instances (default: 1).
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Selecting proper instance types and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:MilanCastro087) counts is vital for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference 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 accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
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11. [Choose Deploy](https://git.aionnect.com) to deploy the model.<br>
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<br>The implementation 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 moment, the design is all set to accept reasoning requests through the [endpoint](https://www.ayurjobs.net). You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.<br>
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<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
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<br>To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the [SageMaker](https://jobs.web4y.online) Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for [garagesale.es](https://www.garagesale.es/author/lashaylaroc/) inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and range 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](http://www.boot-gebraucht.de) predictor<br>
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<br>Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can [produce](https://gitlab.radioecca.org) a [guardrail](http://a21347410b.iask.in8500) using the Amazon Bedrock console or the API, and implement it as shown in the following code:<br>
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<br>Tidy up<br>
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<br>To prevent unwanted charges, complete the actions in this area to clean up your resources.<br>
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<br>Delete the Amazon Bedrock [Marketplace](http://101.231.37.1708087) deployment<br>
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<br>If you released the model using [Amazon Bedrock](https://washcareer.com) 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 implementations.
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2. In the Managed releases section, find the endpoint you wish to delete.
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3. Select the endpoint, and on the Actions menu, pick Delete.
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4. Verify the endpoint details to make certain you're deleting the appropriate implementation: 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 deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
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<br>Conclusion<br>
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<br>In this post, we checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://heli.today) now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.<br>
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<br>About the Authors<br>
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://coverzen.co.zw) business build ingenious options utilizing AWS services and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning performance of large language models. In his spare time, Vivek enjoys hiking, seeing films, and trying various cuisines.<br>
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<br>[Niithiyn Vijeaswaran](https://git.lodis.se) is a Generative [AI](https://gogs.2dz.fi) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://www.fightdynasty.com) [accelerators](https://careerjunction.org.in) (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://horizonsmaroc.com) with the Third-Party Model Science team at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.prime.cv) center. She is passionate about building solutions that assist clients accelerate their [AI](https://www.luckysalesinc.com) journey and [unlock business](https://code.thintz.com) value.<br>
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