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<br>Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock [Marketplace](https://git.pandaminer.com) and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://www.emploitelesurveillance.fr)'s first-generation frontier model, DeepSeek-R1, in addition to the [distilled variations](https://git.saphir.one) ranging from 1.5 to 70 billion specifications to construct, experiment, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:PHZIsis067429) and responsibly scale your generative [AI](http://112.124.19.38:8080) concepts on AWS.<br> |
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<br>In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs also.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://bandbtextile.de) that utilizes reinforcement discovering to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating feature is its support knowing (RL) step, which was used to improve the design's reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually enhancing both significance and [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=995449) clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's geared up to break down complex queries and reason through them in a detailed manner. This directed reasoning process allows the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured responses while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be [incorporated](https://careers.cblsolutions.com) into different workflows such as agents, sensible reasoning and information interpretation tasks.<br> |
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, enabling efficient reasoning by routing inquiries to the most [relevant](https://www.bakicicepte.com) expert "clusters." This approach allows the design to [concentrate](https://boonbac.com) on different issue domains while maintaining overall [efficiency](https://zapinacz.pl). DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 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 upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient models to simulate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher 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 suggest deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and examine designs against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://willingjobs.com) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DannielleDixson) select Amazon SageMaker, and verify you're [utilizing](https://jobs.superfny.com) ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit increase, develop a limit boost demand and reach out 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 correct AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations 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, avoid damaging material, and assess designs against crucial safety requirements. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and [design reactions](https://bio.rogstecnologia.com.br) released on Amazon Bedrock [Marketplace](https://kcshk.com) and SageMaker JumpStart. You can create a [guardrail utilizing](https://24frameshub.com) the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the [GitHub repo](https://www.mapsisa.org).<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 through the ApplyGuardrail API. If the input passes the [guardrail](https://www.lshserver.com3000) check, it's sent out to the model for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is [returned suggesting](https://classtube.ru) the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections show 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 offers you access to over 100 popular, emerging, and [specialized structure](https://gitlab.appgdev.co.kr) 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, pick Model catalog under Foundation designs in the [navigation pane](https://ambitech.com.br). |
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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](https://eelam.tv) APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.<br> |
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<br>The model detail page supplies vital details about the design's abilities, pricing structure, and application guidelines. You can discover detailed usage instructions, including sample API calls and code bits for integration. The design supports different text generation tasks, including material development, code generation, and question answering, using its reinforcement learning optimization and CoT thinking capabilities. |
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The page also consists of deployment alternatives and licensing details to help you start with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, select Deploy.<br> |
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<br>You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be [pre-populated](http://hitq.segen.co.kr). |
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4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Number of circumstances, enter a number of circumstances (in between 1-100). |
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6. For [Instance](https://kod.pardus.org.tr) type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. |
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Optionally, [links.gtanet.com.br](https://links.gtanet.com.br/zarakda51931) you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For a lot of [utilize](http://xn--mf0bm6uh9iu3avi400g.kr) cases, the [default settings](https://juryi.sn) will work well. However, for production implementations, you may desire to review these settings to align with your organization's security and compliance requirements. |
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7. Choose Deploy to start using the design.<br> |
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<br>When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play area to access an interactive user interface where you can try out different triggers and change design criteria like temperature level and optimum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, material for inference.<br> |
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<br>This is an excellent method to check out the model's reasoning and text generation abilities before integrating it into your applications. The [play ground](https://dimans.mx) supplies instant feedback, [assisting](http://git.zonaweb.com.br3000) you comprehend how the design reacts to numerous inputs and letting you tweak your [triggers](https://spudz.org) for optimum results.<br> |
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<br>You can rapidly check the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require 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 demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 design through [Amazon Bedrock](http://www.vpsguards.co) [utilizing](http://bingbinghome.top3001) the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock [console](http://116.205.229.1963000) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends out a request to create text 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) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's [explore](https://mission-telecom.com) both approaches to assist you select the technique that best fits your requirements.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following steps to 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, choose JumpStart in the navigation pane.<br> |
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<br>The model browser shows available designs, with details like the service provider name and model abilities.<br> |
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. |
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Each design card shows key details, including:<br> |
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<br>- Model name |
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[- Provider](https://lensez.info) name |
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- Task category (for example, Text Generation). |
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Bedrock Ready badge (if appropriate), indicating that this model 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 model details page consists of the following details:<br> |
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<br>- The model name and service provider 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 consists of crucial details, such as:<br> |
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<br>- Model description. |
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- License details. |
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- Technical requirements. |
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- Usage guidelines<br> |
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<br>Before you deploy the design, it's recommended to review the model details and license terms to confirm compatibility with your use case.<br> |
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<br>6. Choose Deploy to proceed with implementation.<br> |
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<br>7. For Endpoint name, utilize the immediately generated name or create a custom one. |
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8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge). |
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9. For instance count, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:ElviraLamarr892) go into the number of instances (default: 1). |
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Selecting appropriate circumstances types and counts is essential for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. |
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10. Review all setups for precision. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. |
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11. Choose Deploy to release the model.<br> |
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<br>The deployment procedure can take several minutes to complete.<br> |
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<br>When deployment is complete, [wiki-tb-service.com](http://wiki-tb-service.com/index.php?title=Benutzer:Milla01Z3855169) your endpoint status will change to InService. At this point, the design is prepared to accept reasoning requests through the endpoint. You can keep track of the release development on the SageMaker [console Endpoints](https://gitea.gm56.ru) page, which will display appropriate metrics and status details. When the deployment is complete, 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 using the SageMaker Python SDK<br> |
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<br>To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that demonstrates how to [release](https://wiki.awkshare.com) and use DeepSeek-R1 for 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 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid undesirable charges, finish the steps in this area to clean up your resources.<br> |
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<br>Delete the Amazon Bedrock [Marketplace](https://jobsantigua.com) release<br> |
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<br>If you deployed the model using Amazon Bedrock Marketplace, total the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. |
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2. In the Managed releases area, locate the endpoint you wish to erase. |
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3. Select the endpoint, and on the [Actions](https://ezworkers.com) menu, [pick Delete](https://lubuzz.com). |
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4. Verify the endpoint details to make certain you're deleting 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 design you released will sustain expenses if you leave it running. Use the following code to erase 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 deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 helps emerging generative [AI](https://geoffroy-berry.fr) companies construct innovative solutions utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of big language designs. In his complimentary time, Vivek takes pleasure in treking, enjoying motion pictures, and trying various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://maarifatv.ng) Specialist Solutions Architect with the Third-Party Model [Science team](https://zapinacz.pl) at AWS. His [location](https://videoflixr.com) of focus is AWS [AI](https://pakkjob.com) [accelerators](https://gitlab.steamos.cloud) (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](https://forum.freeadvice.com) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for [Amazon SageMaker](https://git.silasvedder.xyz) JumpStart, SageMaker's artificial intelligence and generative [AI](http://124.129.32.66:3000) hub. She is enthusiastic about building services that assist clients accelerate their [AI](https://gitcq.cyberinner.com) journey and unlock organization value.<br> |
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