Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are delighted to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://careerconnect.mmu.edu.my)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://peopleworknow.com) ideas on AWS.<br> |
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<br>In this post, we [demonstrate](https://starfc.co.kr) how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs as well.<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](http://kousokuwiki.org) that uses reinforcement learning to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying [feature](https://git.marcopacs.com) is its reinforcement knowing (RL) step, which was utilized to [fine-tune](https://youslade.com) the design's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's equipped to break down intricate inquiries and reason through them in a [detailed](https://myjobapply.com) way. This assisted thinking process allows the model to produce more accurate, transparent, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be [integrated](http://120.196.85.1743000) into numerous workflows such as representatives, sensible thinking and data analysis tasks.<br> |
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, making it possible for [effective](http://111.231.76.912095) inference by routing inquiries to the most pertinent professional "clusters." This approach permits the design to concentrate on different issue domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs providing](http://115.236.37.10530011) 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient models to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher model.<br> |
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<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://remotejobsint.com) design, we advise releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and evaluate models against key safety criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, [enhancing](https://gitea.nasilot.me) user experiences and standardizing security controls across your generative [AI](https://git.viorsan.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, [pick Amazon](https://git.dsvision.net) SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 [xlarge circumstances](https://www.ubom.com) in the AWS Region you are releasing. To ask for a limit boost, create a limitation increase demand and reach out to your account group.<br> |
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<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Establish approvals to use 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 permits you to introduce safeguards, prevent harmful content, and assess designs against essential safety criteria. You can execute security procedures for the DeepSeek-R1 model utilizing the Amazon [Bedrock ApplyGuardrail](https://repo.correlibre.org) API. This allows you to [apply guardrails](http://8.137.103.2213000) to assess user inputs and design actions 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 develop the guardrail, see the GitHub repo.<br> |
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<br>The general [circulation](https://cambohub.com3000) includes the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the final outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place 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](http://dev.zenith.sh.cn) 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, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation models 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 does not support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.<br> |
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<br>The design detail page offers important details about the model's abilities, prices structure, and implementation guidelines. You can discover detailed usage guidelines, consisting of sample API calls and code bits for combination. The model supports different text generation jobs, [consisting](http://89.251.156.112) of content creation, code generation, and question answering, utilizing its support learning optimization and CoT reasoning capabilities. |
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The page also consists of release choices and licensing details to help you get started 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 prompted to [configure](https://bug-bounty.firwal.com) the release details for DeepSeek-R1. The design ID will be pre-populated. |
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4. For Endpoint name, go into an [endpoint](https://ashawo.club) name (in between 1-50 alphanumeric characters). |
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5. For Number of circumstances, enter a variety of circumstances (in between 1-100). |
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6. For example type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. |
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Optionally, you can configure advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may wish to review these settings to align with your organization's security and compliance requirements. |
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7. Choose Deploy to start utilizing the model.<br> |
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<br>When the implementation is complete, you can check 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 user interface where you can try out various triggers and adjust design specifications like temperature level and optimum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For instance, content for reasoning.<br> |
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<br>This is an excellent method to explore the model's reasoning and text generation abilities before integrating it into your applications. The playground offers instant feedback, helping you comprehend how the model reacts to [numerous](https://jskenglish.com) inputs and letting you fine-tune your prompts for [optimum outcomes](https://yourecruitplace.com.au).<br> |
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<br>You can quickly check the model in the [playground](https://git.itbcode.com) through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you [require](http://124.192.206.823000) 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 demonstrates how to perform reasoning using a deployed DeepSeek-R1 model through [Amazon Bedrock](http://144.123.43.1382023) using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends out a demand 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, integrated algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's [explore](https://jskenglish.com) both approaches to help you select the technique that best fits your needs.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, select Studio in the navigation pane. |
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2. First-time users will be prompted to develop 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 model web browser shows available models, with details like the provider name and model capabilities.<br> |
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. |
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Each design 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 example, Text Generation). |
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Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model<br> |
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<br>5. Choose the design card to view the design details page.<br> |
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<br>The model details page includes the following details:<br> |
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<br>- The design name and [service provider](https://git.noisolation.com) details. |
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Deploy button to deploy the design. |
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About and Notebooks tabs with detailed details<br> |
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<br>The About tab includes crucial details, such as:<br> |
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<br>- Model description. |
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- License details. |
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- Technical specifications. |
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- Usage standards<br> |
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<br>Before you deploy the design, it's suggested to review the model details and license terms to verify 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 [pipewiki.org](https://pipewiki.org/wiki/index.php/User:TOUJocelyn) Endpoint name, utilize the instantly produced name or create a custom one. |
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8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, enter the number of instances (default: 1). |
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Selecting proper instance types and counts is crucial for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. |
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10. Review all configurations for precision. For this design, we strongly suggest [sticking](https://gst.meu.edu.jo) to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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11. Choose Deploy to deploy 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 deployment is total, your [endpoint status](http://120.77.2.937000) will alter to InService. At this point, the model is prepared to accept reasoning demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can invoke 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 get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is [supplied](https://svn.youshengyun.com3000) in the Github here. You can clone the note pad and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:CarmelPerdue) 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 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 develop a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:<br> |
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<br>Clean up<br> |
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<br>To prevent unwanted charges, complete the [actions](http://work.diqian.com3000) in this area to clean up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace deployment<br> |
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<br>If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace [implementations](https://washcareer.com). |
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2. In the Managed releases area, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) locate the endpoint you want to erase. |
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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 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](http://47.120.16.1378889) model you released will sustain costs if you leave it [running](https://seconddialog.com). Use the following code to delete the endpoint if you desire 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 begun. For more details, describe 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](https://navar.live) JumpStart.<br> |
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<br>About the Authors<br> |
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<br>[Vivek Gangasani](http://www.zeil.kr) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://solegeekz.com) business construct innovative options utilizing AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and the reasoning efficiency of big [language models](https://redebuck.com.br). In his [leisure](https://yezidicommunity.com) time, Vivek takes pleasure in treking, watching motion pictures, and attempting various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://www.xyais.cn) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://lr-mediconsult.de) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [Bioinformatics](http://digitalmaine.net).<br> |
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<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://lr-mediconsult.de) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, [SageMaker's](https://www.keyfirst.co.uk) artificial intelligence and generative [AI](http://8.140.200.236:3000) hub. She is enthusiastic about constructing solutions that help consumers accelerate their [AI](https://gitlab.reemii.cn) journey and unlock service value.<br> |
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