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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](https://jobs.ofblackpool.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](https://www.sedatconsultlimited.com) concepts on AWS.<br>
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<br>In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the models 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) developed by DeepSeek [AI](https://rabota.newrba.ru) that utilizes reinforcement learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its reinforcement knowing (RL) step, which was utilized to improve the design's responses beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's equipped to break down complex inquiries and factor through them in a detailed manner. This directed thinking process allows the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to create structured actions while concentrating on interpretability and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1078448) user interaction. With its [wide-ranging capabilities](https://property.listatto.ca) DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, logical thinking and data interpretation jobs.<br>
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<br>DeepSeek-R1 uses a Mix of [Experts](http://www.forwardmotiontx.com) (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, enabling efficient reasoning by routing queries to the most appropriate professional "clusters." This method permits the model to concentrate on various issue domains while maintaining overall effectiveness. 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 circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 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](http://114.55.169.153000) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective designs to simulate the habits and thinking patterns of the larger DeepSeek-R1 design, utilizing 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://ahlamhospitalityjobs.com) design, we suggest deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and assess models against crucial security criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:DemiStilwell) Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://oakrecruitment.uk) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 model, 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, select Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the [AWS Region](http://39.106.177.1608756) you are [releasing](http://101.200.33.643000). To request a limitation boost, produce a limitation increase request and connect to your account group.<br>
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<br>Because you will be [releasing](http://gitlab.hupp.co.kr) this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish permissions to utilize guardrails for [disgaeawiki.info](https://disgaeawiki.info/index.php/User:MariaKuehner) material 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 harmful material, and [assess models](https://optimiserenergy.com) against key security criteria. You can execute security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and design reactions [released](https://git.yingcaibx.com) on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock [console](https://carvidoo.com) or the API. For the example code to create the guardrail, see the GitHub repo.<br>
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<br>The basic 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 check, it's sent out to the model 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 outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas demonstrate reasoning using this API.<br>
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
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<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and [garagesale.es](https://www.garagesale.es/author/lucamcrae20/) specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) total the following steps:<br>
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<br>1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane.
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At the time of composing 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 company and pick the DeepSeek-R1 model.<br>
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<br>The model detail page provides important details about the design's abilities, rates structure, and execution guidelines. You can discover detailed usage guidelines, including sample API calls and code snippets for [combination](https://hinh.com). The design supports different text generation jobs, consisting of material development, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities.
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The page likewise includes deployment alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications.
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3. To begin using DeepSeek-R1, pick Deploy.<br>
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<br>You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
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5. For Variety of instances, go into a number of [instances](https://opedge.com) (in between 1-100).
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6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
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Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For a lot of [utilize](http://125.ps-lessons.ru) cases, the default settings will work well. However, for production implementations, you may wish to examine these settings to align with your company's security and [compliance](https://bikapsul.com) requirements.
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7. Choose Deploy to begin [utilizing](http://testyourcharger.com) the design.<br>
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<br>When the release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
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8. Choose Open in playground to access an interactive interface where you can try out different prompts and adjust design parameters like temperature and maximum length.
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.<br>
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<br>This is an outstanding method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play area provides instant feedback, helping you comprehend how the [design reacts](http://120.92.38.24410880) to different inputs and letting you fine-tune your triggers for ideal results.<br>
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<br>You can [rapidly test](https://cats.wiki) the model in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
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<br>Run inference using guardrails with the deployed DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using 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 produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, [configures reasoning](http://git.motr-online.com) specifications, and sends 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, built-in algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production utilizing either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free approaches: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the approach 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 JumpStart:<br>
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<br>1. On the SageMaker console, choose Studio in the [navigation](https://vacaturebank.vrijwilligerspuntvlissingen.nl) pane.
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2. First-time users will be [prompted](https://africasfaces.com) to develop 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 shows 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 design card.
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Each design card shows essential details, including:<br>
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<br>[- Model](https://gitlabdemo.zhongliangong.com) 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 relevant), indicating that this model can be signed up with Amazon Bedrock, enabling you to use [Amazon Bedrock](http://121.37.138.2) APIs to invoke 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 design details page includes the following details:<br>
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<br>- The design name and [supplier details](https://upmasty.com).
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Deploy button to deploy the model.
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About and Notebooks tabs with detailed details<br>
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<br>The About [tab consists](http://121.28.134.382039) of essential details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical specs.
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- Usage guidelines<br>
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<br>Before you release the model, it's recommended to evaluate the design details and license terms to verify compatibility with your use case.<br>
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<br>6. Choose Deploy to proceed with deployment.<br>
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<br>7. For Endpoint name, utilize the [automatically produced](https://gitea.linuxcode.net) name or develop a custom-made one.
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8. For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial circumstances count, get in the variety of circumstances (default: 1).
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Selecting suitable [circumstances](http://kodkod.kr) types and counts is essential for expense and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning 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 model, we strongly advise sticking to SageMaker JumpStart default [settings](http://47.103.112.133) 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 implementation procedure can take a number of minutes to complete.<br>
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<br>When deployment is complete, your endpoint status will alter to InService. At this point, the model is ready to accept inference demands through the [endpoint](https://git.kuyuntech.com). You can monitor the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the [release](http://148.66.10.103000) is total, you can invoke the model using a SageMaker runtime client 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 going with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a [detailed](https://recruitment.econet.co.zw) code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and run 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 inference with your SageMaker JumpStart predictor<br>
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<br>Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:<br>
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<br>Tidy up<br>
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<br>To [prevent undesirable](https://www.guidancetaxdebt.com) charges, complete the actions in this section to tidy up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace implementation<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 designs in the navigation pane, choose Marketplace deployments.
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2. In the Managed implementations section, find the endpoint you desire to erase.
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3. Select the endpoint, and on the Actions menu, select 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 costs 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 explored 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 start. For more details, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:LoreenErtel66) describe Use [Amazon Bedrock](https://stationeers-wiki.com) tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with Amazon SageMaker JumpStart.<br>
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<br>About the Authors<br>
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://gamberonmusic.com) companies build innovative solutions utilizing [AWS services](https://leicestercityfansclub.com) and accelerated calculate. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning efficiency of large language models. In his downtime, Vivek delights in hiking, watching movies, and attempting different foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://172.105.135.218) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://gitee.mmote.ru) accelerators (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](http://git.taokeapp.net:3000) with the Third-Party Model Science team at AWS.<br>
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<br>Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://igita.ir) center. She is passionate about developing services that help clients accelerate their [AI](http://120.79.27.232:3000) journey and [unlock service](http://122.51.46.213) worth.<br>
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