Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://git.skyviewfund.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://talentup.asia) concepts on AWS.<br> |
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<br>In this post, we [demonstrate](http://111.2.21.14133001) how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models as well.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://jobs.ezelogs.com) that uses support finding out to improve reasoning capabilities through a multi-stage training [procedure](https://nerm.club) from a DeepSeek-V3-Base foundation. An essential identifying function is its support learning (RL) step, which was utilized to refine the model's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt better to user [feedback](https://git.howdoicomputer.lol) and objectives, eventually enhancing both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's geared up to break down complex questions and factor through them in a detailed manner. This assisted thinking procedure permits the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually [captured](https://play.hewah.com) the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as agents, logical reasoning and information interpretation jobs.<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 allows activation of 37 billion specifications, enabling efficient reasoning by routing queries to the most pertinent expert "clusters." This technique allows the model to concentrate on different problem domains while maintaining total performance. 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 comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 [distilled designs](http://safepine.co3000) bring the reasoning abilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, 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 model, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and evaluate designs against crucial safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://massivemiracle.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy 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 validate you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation boost, produce a [limit increase](https://smaphofilm.com) demand and connect to your account group.<br> |
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<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize guardrails for content 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 hazardous content, and evaluate designs against essential safety criteria. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model actions released on [Amazon Bedrock](https://www.flytteogfragttilbud.dk) [Marketplace](http://47.101.207.1233000) and SageMaker JumpStart. You can create a guardrail utilizing 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 circulation involves the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for inference. After receiving 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 showing the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show inference utilizing this API.<br> |
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br> |
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<br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and [specialized foundation](https://apk.tw) designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, select Model brochure under [Foundation designs](http://107.172.157.443000) in the navigation pane. |
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At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other [Amazon Bedrock](https://vids.nickivey.com) tooling. |
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2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.<br> |
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<br>The design detail page offers necessary details about the model's abilities, [classificados.diariodovale.com.br](https://classificados.diariodovale.com.br/author/tawnyafoti/) prices structure, and application standards. You can find detailed use guidelines, consisting of sample API calls and code bits for integration. The model supports numerous text generation tasks, consisting of material development, [gratisafhalen.be](https://gratisafhalen.be/author/marilyn97k3/) code generation, and question answering, utilizing its support finding out optimization and CoT reasoning capabilities. |
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The page likewise includes deployment options and licensing details to help you start with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, pick Deploy.<br> |
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<br>You will be triggered to configure the deployment details for DeepSeek-R1. The model 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, enter a number of instances (between 1-100). |
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6. For Instance type, choose your circumstances type. For ideal 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 infrastructure settings, consisting of virtual private cloud (VPC) networking, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MistyGoodenough) service function approvals, and encryption settings. For many use cases, the [default](https://kaamdekho.co.in) settings will work well. However, for [production](http://81.71.148.578080) implementations, you might desire to review these settings to line up with your company's security and [compliance](https://jobsspecialists.com) requirements. |
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7. Choose Deploy to begin utilizing the design.<br> |
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<br>When the release is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. |
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8. Choose Open in play area to access an interactive interface where you can try out different triggers and adjust design specifications 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 optimum results. For instance, content for inference.<br> |
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<br>This is an outstanding way to explore the design's reasoning and text generation abilities before integrating it into your applications. The playground supplies immediate feedback, helping you comprehend how the design responds to numerous inputs and letting you tweak your triggers for optimum results.<br> |
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<br>You can quickly [evaluate](https://git.unicom.studio) the design in the play area through the UI. However, to conjure up the released design 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 released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon [Bedrock console](https://gogs.eldarsoft.com) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to [execute guardrails](http://175.178.113.2203000). The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends a request to create 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](http://47.119.27.838003) intelligence (ML) hub with FMs, integrated 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 deploy 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 methods: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you select the method that best matches your needs.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, pick Studio in the navigation pane. |
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2. First-time users will be prompted to create a domain. |
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3. On the [SageMaker Studio](http://62.234.217.1373000) console, pick JumpStart in the navigation pane.<br> |
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<br>The model [internet browser](http://185.87.111.463000) displays available models, with details like the supplier name and model capabilities.<br> |
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. |
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Each model card reveals key details, consisting of:<br> |
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<br>[- Model](http://81.71.148.578080) 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 suitable), indicating that this design can be [registered](https://tangguifang.dreamhosters.com) with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model<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 design name and service provider details. |
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[Deploy button](https://melaninbook.com) 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 important details, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:MickeySwisher) 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 design, it's recommended to examine the model details and license terms to validate compatibility with your use case.<br> |
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<br>6. Choose Deploy to continue with release.<br> |
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<br>7. For Endpoint name, utilize the automatically produced name or produce a custom one. |
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8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, go into the number of instances (default: 1). |
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Selecting suitable instance types and counts is crucial for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. |
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10. Review all setups for precision. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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11. Choose Deploy to release the design.<br> |
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<br>The deployment process can take a number of minutes to complete.<br> |
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<br>When release is complete, your endpoint status will change to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status [details](https://bdenc.com). When the [deployment](https://employme.app) is total, you can invoke the design 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 utilizing the [SageMaker Python](http://35.207.205.183000) SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a [detailed](https://git.thunraz.se) code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is [supplied](https://www.ch-valence-pro.fr) 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 reasoning with your SageMaker JumpStart predictor<br> |
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<br>Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid [unwanted](https://wisewayrecruitment.com) charges, complete the actions in this section 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 model using [Amazon Bedrock](https://iamtube.jp) Marketplace, total the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. |
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2. In the [Managed releases](http://sanaldunyam.awardspace.biz) area, find the endpoint you wish to delete. |
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3. Select the endpoint, and on the Actions menu, choose Delete. |
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4. Verify the endpoint details to make certain you're erasing the proper deployment: 1. Endpoint name. |
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2. Model name. |
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3. Endpoint status<br> |
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<br>Delete the SageMaker JumpStart predictor<br> |
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<br>The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want to stop [sustaining charges](https://ospitalierii.ro). 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 now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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://emplealista.com) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://kcshk.com) business construct innovative options using AWS services and accelerated compute. Currently, he is concentrated on establishing techniques for fine-tuning and [optimizing](http://101.43.129.2610880) the inference efficiency of big [language models](http://app.ruixinnj.com). In his spare time, Vivek delights in treking, viewing motion pictures, and attempting various cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://test.manishrijal.com.np) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://candidates.giftabled.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://gitlab.optitable.com) with the Third-Party Model Science group at AWS.<br> |
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<br>[Banu Nagasundaram](http://101.231.37.1708087) leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.polycompsol.com:3000) center. She is enthusiastic about developing services that help clients accelerate their [AI](https://visorus.com.mx) journey and unlock business value.<br> |
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