Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are thrilled 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](http://sgvalley.co.kr)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and [responsibly scale](http://xn--vk1b975azoatf94e.com) your generative [AI](https://thesecurityexchange.com) ideas 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 models too.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://www.eruptz.com) that uses [reinforcement finding](https://photohub.b-social.co.uk) out to enhance thinking [capabilities](https://git.mitsea.com) through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement knowing (RL) step, which was used to improve the design's actions beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's equipped to break down complex inquiries and factor through them in a detailed way. This assisted thinking process enables the design to produce more precise, transparent, and detailed answers. This [model integrates](https://www.koumii.com) RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation model that can be integrated into different workflows such as agents, sensible thinking and information analysis jobs.<br> |
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient reasoning by routing inquiries to the most relevant specialist "clusters." This approach enables the design to concentrate on different issue domains while maintaining total effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. 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 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 procedure of training smaller, more effective designs to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor design.<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 design, we suggest deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and assess designs against crucial security requirements. 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 produce multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](http://yezhem.com:9030) applications.<br> |
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<br>Prerequisites<br> |
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<br>To release the DeepSeek-R1 design, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) 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 SageMaker, and [confirm](http://home.rogersun.cn3000) you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limitation increase, develop a limitation boost demand and connect to your account group.<br> |
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to [utilize Amazon](https://tv.goftesh.com) 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, avoid damaging material, and assess models against [essential](https://www.referall.us) safety criteria. You can carry out security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model reactions [released](https://jobs.fabumama.com) on Amazon Bedrock [Marketplace](http://git.huxiukeji.com) and SageMaker JumpStart. You can create a guardrail utilizing 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 basic flow involves 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 check, it's sent to the design for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show reasoning utilizing this API.<br> |
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br> |
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<br>Amazon [Bedrock](https://git.becks-web.de) [Marketplace](https://mediawiki1334.00web.net) gives you access to over 100 popular, emerging, and [specialized structure](https://www.refermee.com) models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. |
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At the time of composing this post, you can utilize the InvokeModel API to invoke the design. 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 design.<br> |
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<br>The model detail page offers essential details about the design's capabilities, prices structure, and application guidelines. You can find detailed usage guidelines, consisting of sample API calls and code bits for integration. The model supports various text generation jobs, consisting of content creation, code generation, and question answering, using its reinforcement finding out optimization and CoT thinking capabilities. |
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The page likewise consists of implementation options and licensing details to help you start with DeepSeek-R1 in your applications. |
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3. To begin using DeepSeek-R1, choose Deploy.<br> |
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<br>You will be triggered to configure the release details for DeepSeek-R1. The model ID will be pre-populated. |
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4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Number of instances, go into a number of circumstances (in between 1-100). |
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6. For Instance type, pick your instance type. For optimal 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 infrastructure settings, consisting of virtual private cloud (VPC) networking, service role permissions, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may wish to [evaluate](https://nexthub.live) 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 total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. |
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8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and adjust model specifications like temperature and maximum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, content for reasoning.<br> |
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<br>This is an excellent method to check out the design's thinking and text generation abilities before integrating it into your applications. The play area supplies instant feedback, assisting you comprehend how the design reacts to various inputs and letting you tweak your triggers for optimum outcomes.<br> |
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<br>You can quickly evaluate the model in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run inference using guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a demand to produce 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](https://career.logictive.solutions) is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML [services](https://myclassictv.com) that you can [release](https://bogazicitube.com.tr) with simply a couple of clicks. With SageMaker JumpStart, you can tailor [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) pre-trained designs to your use case, with your information, and deploy them into [production](http://42.192.80.21) using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient methods: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the [SageMaker Python](https://git.purwakartakab.go.id) SDK. Let's explore both approaches to help you pick the technique that finest 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 steps to release DeepSeek-R1 [utilizing SageMaker](https://playvideoo.com) 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, pick JumpStart in the navigation pane.<br> |
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<br>The design web browser displays available models, with details like the service provider name and model abilities.<br> |
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. |
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Each model card reveals essential details, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task classification (for example, Text Generation). |
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[Bedrock Ready](https://rpcomm.kr) badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, allowing 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 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 company 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 of important 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 release the design, it's suggested to examine the model details and license terms to validate compatibility with your usage case.<br> |
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<br>6. Choose Deploy to [proceed](http://85.214.112.1167000) with implementation.<br> |
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<br>7. For Endpoint name, utilize the automatically generated name or create a customized 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 instance count, enter the number of instances (default: 1). |
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Selecting appropriate instance types and counts is essential for cost and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning 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 strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. |
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11. Choose Deploy to deploy the model.<br> |
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<br>The deployment procedure can take several minutes to complete.<br> |
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<br>When release is complete, your endpoint status will change to InService. At this point, the design is ready to accept inference [requests](https://iesoundtrack.tv) through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can invoke the design using a SageMaker runtime customer and incorporate 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 get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed [AWS authorizations](https://followmypic.com) and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.<br> |
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<br>You can run extra requests against the predictor:<br> |
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<br>Implement guardrails and run reasoning with your [SageMaker JumpStart](https://www.imf1fan.com) predictor<br> |
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<br>Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart [predictor](https://0miz2638.cdn.hp.avalon.pw9443). You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid unwanted charges, complete the steps in this section 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 using Amazon Bedrock Marketplace, complete the following actions:<br> |
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<br>1. On the [Amazon Bedrock](http://stockzero.net) console, under Foundation models in the navigation pane, pick Marketplace implementations. |
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2. In the Managed deployments area, find the endpoint you desire 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 [deleting](https://git.lunch.org.uk) the proper implementation: 1. [Endpoint](https://vmi528339.contaboserver.net) name. |
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2. Model name. |
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3. [Endpoint](https://www.contraband.ch) status<br> |
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<br>Delete the SageMaker JumpStart predictor<br> |
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<br>The SageMaker JumpStart design you deployed will sustain expenses if you leave it . Use the following code to erase 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 explored how you can access and deploy the DeepSeek-R1 model utilizing [Bedrock](http://125.ps-lessons.ru) Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, [Amazon SageMaker](https://globalabout.com) 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](https://realestate.kctech.com.np) at AWS. He assists emerging generative [AI](https://www.yiyanmyplus.com) companies construct ingenious options using AWS services and accelerated compute. Currently, he is [concentrated](http://123.60.19.2038088) on establishing techniques for [fine-tuning](https://goodprice-tv.com) and enhancing the reasoning performance of large language models. In his totally free time, Vivek enjoys hiking, seeing movies, and trying different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a [Generative](https://alumni.myra.ac.in) [AI](http://wowonder.technologyvala.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://lovelynarratives.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br> |
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://mcn-kw.com) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and strategic collaborations for [gratisafhalen.be](https://gratisafhalen.be/author/tamikalaf18/) Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://code.exploring.cn) hub. She is passionate about building services that help clients accelerate their [AI](http://bertogram.com) journey and unlock company value.<br> |
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